bluuebunny/crossref_metadata_2025 huggingface dataset:
Statistical Learning Methods for Predictive Analytics in Engineering Management
Authors: Dr. Tirumala Haripriya  , Dr. Dattatreya P. Mankame  , Dr. Basavaraj Patil  , Abhijeet Das  , Nidhi Jindal  , Gauri Jindal
Published: December 2024

The purpose of this study is to present the application of statistical learning methods for predictive analytics within engineering management, based on enhancing decision-making and achieving operational efficiency. By using four core algorithms that were applied in the study-Neural Networks, Support Vector Machines (SVM), Linear Regression, and Decision Trees-the performance evaluation with respect to predicting major key engineering outcomes is established as follows: Results reveal that Neural Networks had an accuracy of 94.5%, showing robustness to manage complex data patterns. The second best result was SVM, with an accuracy of 91.2%, but it excelled in nonlinear data and had a greater demand for computation. Linear Regression and Decision Trees scored 78.4% and 82.3%, respectively, indicating that they were good for simplicity and interpretability but not capable with complex data. The results of the comparative analysis show that Neural Networks and SVM are good for predictive applications in engineering management. This study recommends further research into hybrid models that achieve the best balance between accuracy and interpretability to support improvements in resource allocation and productivity. This research adds value by shedding light on how statistical learning can be practically applied to engineering practice as a foundation for data-driven strategic decision-making.
A Deep Learning Multifractal Texture Analysis Using Principal Line Extraction Approach for Palmprint Recognition System
Authors: Abirami B  , Krishnaveni K
Published: December 2024

To make the methodical Palmprint Recognition System (PRS), this research proposed an unique Deep Learning classifier using the palm hand’s principal lines extraction approach and multifractal texture analysis approach. To reveal the efficient biometric security, a Deep Learning Multifractal Texture Analysis for Palmprint Recognition System (DLMTA-PRS) has been suggested. In DLMTA-PRS system, exact principal lines of Two–Dimensional Palmprint Region of Interest (2DPROI) image are extracted using morphological operations and an edge detection algorithm in a peculiar manner. Then, Feature values of 2DPROI image are fetched using multifractal texture analysis approach. To perform this approach, Box-counting and Gliding-Box algorithms are performed and the feature vector is created. The feature vector is classified using the proposed Convolution Neural Network (CNNNet) classifier approach to get the higher authentication security. The multi-spectral 2DPROI image database has been utilized for this research which is acquired from the PolyU, the Hong Kong Polytechnic University in Hong Kong. The suggested DLMTA-PRS system underwent scrutiny as well as proved the best of its by evaluating the DLMTA-PRS system using numerous criterias with the achievement of getting 99.25% accurate identification rate.
Analysis of Query Optimization Using Deep Reinforcement Learning Using Particle Swarm Optimization Algorithms
Authors: Karthikeyan M P  , Dr. Krishnaveni K
Published: December 2024

Query optimization is a well-studied problem in the database industry, with numerous solutions proposed over the last several decades. The success of deep reinforcement learning (DRL) has generated new opportunities in query optimization. One of the most difficult tasks in query optimization and query plan generation is determining the order in which join operations between tables are done (i.e. relations). Even if the final results of a query remain identical regardless of join order, the order in which the tables of a query are joined can have a significant impact on query execution time. Deep reinforcement learning, in particular a data-driven method to reasoning about enumeration heuristics, provides a novel algorithmic viewpoint on join enumeration. We must now control what training data the model views and how that data is featured, rather than the standard tunable parameters of a query optimizer. The algorithm makes few assertions about the cost model's structure or the search space's topology. We demonstrate that Q learning optimizes plans well across many different cost models for a small set of training queries. On the TPC-H database, the Query Optimization Algorithms Q-Learning and PSO (Particle Swarm Optimization) are assessed. During the evaluation, the optimizer failed to complete one query within the maximum time permitted, whereas the deep reinforcement learning-based models (Q-Learning) and heuristics model (PSO) managed. Of course, the standard join ordering problem is NP-hard, and practical algorithms use heuristics to make the search for a good plan efficient. A novel method for Query Optimization using Particle Swarm Optimization (QOPSO) and Deep Q Learning (DQL) for parameter tuning of Join Operation Cost and Processing Cost is suggested in this paper.
Disease Detection in Corn or Maize Plant Leaves Using Specim Iq Hyperspectral Imaging and Proposed Dnn Classifiers with Alexnet
Authors: Praba V  , Dr. Krishnaveni K
Published: December 2024

It is essential to identify illnesses in corn and maize plants early on in order to preserve crop health and guarantee agricultural output. This work investigates the detection of plant diseases in leaves using advanced deep learning techniques in conjunction with Specim IQ hyperspectral imaging. We compare the performance of a newly constructed classifier, DeepIncepNet, with other state-of-the-art models, such as InceptionV3, ResNet-50, and ResNet-101. We also present a novel Deep Neural Network (DNN) classifier based on the AlexNet architecture. Preprocessing was done on hyperspectral imaging data to improve image quality and retrieve pertinent characteristics. A large dataset was used to train and verify the classifiers, and the results showed excellent disease detection accuracy. The comparative analysis illustrates the benefits and drawbacks of each model, highlighting the possibility for accurate and effective plant disease diagnosis through the combination of deep learning and hyperspectral imaging—a major improvement over conventional techniques.
A Comprehensive Analysis On The Association Between The KI 67 And P 53 Markers And Different Types Of Brain Tumors
Authors: Manish Tenguria  , Vishal Rajput  , Sanjay Gupta  , Meenu Gupta
Published: December 2024

Gliomas are characterized by mutations in the p53 tumor suppressor gene. The cell proliferation marker Ki-67 is frequently employed in the context of gliomas. The survival rates of individuals with brain tumors exhibit considerable variation, with treatment trials heavily relying on prognostic markers. The utilization of digital pathology and improved immune-histochemical diagnostics allowed for a more accurate assessment of the true significance of the Ki-67 labeling index (LIpredictive) in glioblastomas by eliminating the presence of proliferating non-tumor cells during research. Among these markers, MIB1 (Ki67) stands out as the most popular and practical option. Consequently, the Ki-67 Labeling Index (LI) may possess prognostic and therapeutic implications for patients. The p53 tumor gene (TP53) is the gene that is not frequently altered in various malignancies and is also absent in a variety of different types of brain tumors. The identification of the molecular mechanisms through which p53 mutations cause cancer forms the basis for the development of new treatment strategies. Obtaining more conclusive results could be facilitated by reviewing studies that involve a larger number of patients across multiple centers and have a planned prospective follow-up of longer duration. In future research, it might be feasible to explore whether the incorporation of surgical, histological, and imaging indications could result in a more accurate method for predicting recurrence.
A Novel Geometric Modification To The Regula Falsi Method To Achieve Cubic Convergence Order
Authors: Inderjeet  , Rashmi Bhardwaj
Published: December 2024

Chen and Li (2007) presented a family of quadratically convergent Regula Falsi iterative methods for solving nonlinear equations f(x) =0. Additionally, it is demonstrated there that the iterative point sequence converges to zero. This work aims to accelerate the method convergence from quadratic to cubic forms. This can be achieved by substituting an appropriately specified function, p(x), for the parameter p in Chen and Li's iteration. A convergence theorem is used to determine the cubic convergence of the iterate sequence to the root. The numerical examples show that the proposed method is more efficient and required fewer number of iterations in comparison to Newton's method, Steffensen's method, Regula Falsi method, and those provided in Chen and Li.
Subclasses of Starlike and Convex Functions Involving Pascal Distribution Series
Authors: B. B. Gadekar  , R. P. Pathak  , S. G. Gajbhiv
Published: December 2024

The main object of this paper is to establish connections between various subclasses of normalized analytic univalent functions and Pascal distribution series by applying certain convolution operator involving Pascal distribution series.
Performance Analysis of WSCC 9-Bus System Integrated with Photovoltaic under Load Increment Condition
Authors: Mahit V. Jain  , Rital R. Gajjar
Published: December 2024

The integration of renewable energy sources is pivotal for the modernization of power systems. This paper presents a comprehensive performance analysis of the Western Systems Coordinating Council (WSCC) 9-bus system integrated with photovoltaic (PV) under conditions of load increment. The study aims to evaluate the dynamic behavior and stability of the system, highlighting the effects of increased load demand on the integrated system’s performance. Using a combination of simulation tools and analytical methods, we model the WSCC 9-bus system with integrated PV to assess various performance metrics. The analysis includes the examination of voltage profiles and active and reactive power flow under normal and increased load scenarios. The authors’ results indicate that the integration of PV significantly enhances the system's ability to maintain stable voltage levels, even under substantial load increments. However, the effectiveness of these integrations is highly dependent on the appropriate sizing and placement of the PV within the system. This study provides valuable insights into the operational benefits and challenges of incorporating renewable energy solutions into existing power systems. In the future, it underscores the importance of strategic planning in the deployment of PV to optimize system performance. The findings contribute to the ongoing efforts in achieving a more resilient and sustainable power grid, capable of accommodating higher levels of renewable energy penetration while ensuring reliability. This WSCC-9 bus system is simulated in MATLAB 2021b version.
Admission Allotment Scheme Base Load Balancing Techniques for Clustering Routing in Wireless Sensor Network
Authors: Shahid Thekiya  , Mangesh Nikose  , Prashant Chordiya
Published: December 2024

The Wireless Sensor Network, often known as WSN, has a wide range of applications spanning the commercial, indus-trial, and social spheres. WSN clustering is an efficient and low-cost way to extend the lifespan of a network while simultaneously improving its scalability, reliability and throughput. The performance of the WSN is hindered by lim-ited-power battery-driven sensor nodes and inaccurate cluster head (CH) location during the process of cluster crea-tion. This paper presents the Fuzzy C-mean algorithm (FCM) for clustering as well as the Artificial Bee Colony Algo-rithm (ABC) for CH selection and optimization. Both of these algorithms can be found here. A number of different clustering parameters are taken into consideration by the proposed ABC. These considerations include CH energy balancing, CH load balancing, the energy GINI coefficient, connectivity, as well as intra-cluster and inter-cluster distance. Afterward, an Ant Colony Optimization (ACO) that is more energy efficient is developed to transfer the data from CH to the base station (BS). Further, the novel Admission Allotment Scheme (AAS) it utilized for energy efficient intra-cluster communication to enhance the load balancing of the network. In relationship to the conventional state of the art, the newly suggested method delivers optimum cluster selection, which results in improved network lifespan, packet delivery ratio, and throughput.
A Knowledge Based Grade Prediction System using Machine Learning for Higher Education Institutions
Authors: Prakash. P  , Shivanand S. Gornale  , Rashmi Siddalingappa  , Satish Kumar
Published: December 2024

The enhancement and preservation of standard in the higher education are pivotal for the enduring viability of Higher Education Institutes (HEIs). National Assessment and Accreditation Council (NAAC) in India introduced a new framework for evaluating HEIs in July 2017 based on qualitative and quantitative data analysis and will be assessed and is carried in two ways Data Validation & Verification (DVV) and the onsite peer team visit. The entire Assessment and Accreditation (A&A) process will take the timeline of six to seven months to complete is time consuming and the human intervention. In this proposed work, a predication model using machine learning techniques is developed to assess the performance of HEIs based on the NAAC Criteria within short timeframe and without human intervention. We have used the Multiclass label classification to predict the Key Indicator Qualitative metric score, and the classification based on the total Quantitative & Qualitative Score. The study utilized four distinct algorithms of Machine learning (ML) for classification: Naive Bayes (NB), Random Forest (RT), K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). The SVM classification technique exhibited the highest accuracy at 97%, followed by Random Forest at 94%, among the four classifiers.
Integrating Generative AI in Cloud Computing Architectures: Transformative Impacts on Efficiency and Innovation
Authors: P. R. Sudha Rani
Published: December 2024

To cope with the ever-increasing information processing and enable the developing machinery of human civilization to grow smarter, it becomes necessary to build a cloud computing environment that can process large amounts of data at high speeds and low costs. For neural network-based inference applications, GPUs are commonly used. It is for this reason that we believe large-scale GPU clusters, which are currently indispensable in cloud computing processing, can be expected to solve the problem that they are highly efficient for neural network processing, yet less efficient for other processing methods, including basic users. In this study, we propose deep neural network-based processing technology that can utilize the technology for various processing by converting new processing applications, which are not only neural network-based inference, into multiple tasks that can be executed by GPUs. By making them executable, even conventional data distribution, effective job allocation, etc. can become much easier to achieve, resulting in synergistic improvements in GPU cluster utilization.In addition, users using a cloud that integrates the proposed method can minimize processing costs even when neural network processing and other processing methods are mixed and used, and can spend the budget obtained from that cost savings on realizing new business innovations. It is expected that this approach will profoundly influence the direction of cloud computing business deployments.
Enhancing Cloud Computing Performance with AI/ML and Generative AI: Innovative Approaches and Applications
Authors: Aaluri Seenu
Published: December 2024

In a dynamic and fast-growing cloud computing market, reducing server equipment and energy consumption is the primary goal of cloud technologies. This article describes innovative solutions aimed at improving the efficiency of computing systems using cloud computing, in particular through the use of modern technology such as artificial intelligence and global search algorithms. AI increases the workload of the cloud computing system and accelerates the search for the most optimal solutions. ML is used in classifying various user needs, decisions, workload, and power consumption prediction. It also achieves energy consumption reduction using ML models based on AI/R using generative modeling and reinforcement learning models. The method and decision-making algorithms are presented for the implementation of the system, and the results of the optimizations are analyzed and discussed.The article will be useful for professionals in the field of cloud computing and all areas of artificial intelligence, who will be able to find new ways to reduce the power consumption and heat generation of complex computing systems, thereby increasing their energy efficiency and improving service life. Due to increasing volumes of data migration to cloud platforms, there is a rising demand for cloud computing data centers, which require commensurately vast server hardware equipment. In 2018, the total energy consumption for data center facilities around the world was about 2% of the total power generated, with a significant part of the consumed energy being used to cool the data centers.
Taguchi L8 Orthogonal Array and ANOVA-Based Optimization of Injection Molding Parameters for Reducing Warpage in Set-Top Box Components
Authors: Shon Patil  , Nitin Phafat  , Ashok Keche
Published: December 2024

This study seeks to optimize injection molding process parameters to minimize defects such as warpage and short shots in the manufacturing of Set-Top Box components. The Design of Experiments (DoE) methodology, employing the Taguchi technique and a L8 orthogonal array, examined six essential parameters: flow rate, melt temperature, mold temperature, holding pressure, gate diameter, and runner diameter. The influence and statistical significance of each parameter were evaluated using Analysis of Variance (ANOVA). The findings indicated that melt temperature, mold temperature, and holding pressure are crucial in reducing warpage, with melt temperature accounting for 48.08% of the difference. The mold temperature and holding pressure contributed 21.37% and 6.90%, respectively. The research shows that maximizing these critical parameters is vital for minimizing warpage and short shots, hence improving dimensional accuracy and overall quality of injection-molded components. The results offer significant insights and actionable recommendations for enhancing efficiency and product quality in injection molding processes, laying the groundwork for subsequent study in process optimization.
Formulation and Evaluation of Niosomes of Fortified Bacoside: A Rich Fraction from Bacopa Monnieri for Memory Enhancement
Authors: Shweta Babasaheb Dighe  , Ranawat Mahendra Singh
Published: December 2024

The triterpenoid saponins, bacosides, and bacoside A are the main active ingredients of Bacopa monnieri that have been shown to improve intelligence or cognition. Bacoside A's low aqueous solubility and blood brain barrier (BBB) restriction are the two factors that limit its bioavailability and clinical efficacy for treating neurodegenerative diseases. Colostrum from cows enhances the cognitive benefits of Bacoside A. In order to deliver Bacopa monnieri 's fortified Bacoside A fraction (Fort-BAF) with enhanced oral bioavailability for possible memory-enhancing activity, the goal of this study was to create and assess the best niosomal formulation. Niosomes were created using the thin-film hydration method, and the hydration volume, cholesterol, and span 60 were the main variables that were optimised using a Box-Behnken Design (BBD) with Response Surface Methodology (RSM). The stability and compatibility of Fort-BAF with other formulation ingredients were validated by preformulation studies. Fort-BAF (BN9)'s optimal niosomal formulation was made and described. An in vivo biological evaluation and in vitro release were conducted to investigate the memory-enhancing properties of the optimised niosomal formulation (BN9) in comparison to the Bacoside A rich fraction. With a favourable zeta potential of -28.5 mV, a small particle size of 121.7±3.22 nm, and a high entrapment efficiency of 87.56%, the optimised BN9 showed promising properties that indicated stability and efficacy. According to the Higuchi model, in vitro drug release studies showed that 75.42% of the drug was released in an hour. The niosomes' spherical shape and porous structure were validated by SEM and TEM analyses. According to stability tests, the formulation holds up well over a three-month period. According to in-vivo research findings, BN9 may improve learning and memory. The findings imply that niosomes can greatly improve the Fort-BAF's stability and bioavailability. The potential of niosomal formulations as a viable method for delivering Bacopa monnieri 's Fort-BAF to the brain in neuroprotection and cognitive enhancement treatments is highlighted by this study.
Analysis Of Sinusoidal Loads On A Simply Supported Beam Using Hyperbolic Shear Deformation Theory
Authors: Renu P Pathak  , Mahesh V. Ghotkar  , Satish G. Gajbhiv  , Durgesh H. Tupe
Published: December 2024

Background: The overlaid bar speculation has been approved in a few fields, including sports, flight, medication, and marine developments. Cantilever radiates, fixed radiates, and basically upheld radiates were dissected under different stacking situations. Arrangements were tracked down as relocations and stresses for fixed radiates, just upheld radiates, and different layered cross-utilize covered composite cantilever radiates exposed to variable, uniform, and sinusoidal burdens. Contrasting angle proportions consider fluctuating results concerning relocation, twisting pressure, and shear pressure. The constitutive relationship and balance condition are utilized to process the shear stresses. The virtual dislodging guideline is utilized to decide the covered composite beams exaggerated capability (HYSDT) and relocation field.
Scanning Tunneling Microscopy: A Review
Authors: Barberán José Ricardo Macías
Published: December 2024

The study of surfaces and manipulation of materials of nanometric size is a method widely used in different areas of research. The scanning tunneling microscope (STM) is a valuable instrument that allows working on these two aspects with elements of scientific interest. This article tries to explain the principles and applications of the STM based on attractions and repulsions of atoms at very small distances to collect information, which once processed and analyzed is converted into images visible to the human eye. The review was carried out through the search and analytical reading of scientific papers and publications such as Google Scholar, Scopus, Scielo, Redalyc and Doaj. The contextualization of quantum tunneling was established focused on the importance, applicability and development of the STM which has allowed the new advances developed throughout the 21st century.
Medical Image Segmentation Using Modified U-Net
Authors: Mohd Mahboob Ali Mahmood  , Mohd Sohaib  Ali Mahmood
Published: December 2024

Medical image analysis is for essential in addressing medical puzzles by extracting respective image information from imaging devices to enhance diagnostic results. The current study proposes an Improved U-Net model named the Image Contrast-Based U-Net Segmentation model used to diagnose the skin diseases, as well as identifying the affected areas of the skin for segmentation. In the developed model, the red boundary box frames the areas affected to assist physicians in the identification and diagnosis of different skin diseases. Both of the used datasets ISIC and PH2 were used for training U-Net model and the test results of the model show that it achieves an accuracy of over 98% for identifying the affected pixels. The above system lets a user to upload test images, view original and segmented images, and the results of the same are clearly marked or highlighted on the areas to be affected. In the lamp-model performance comparison on the two sets of data we find equate performances. The underlying proposed system is an efficient, user friendly tool for skin disease identification which can be extended to other medical image segmentation applications coupled with efficiency increase to the diagnostic workflows in clinical practice.
A Comprehensive Framework For Iot, AI, And Machine Learning In Healthcare Analytics
Authors: Dr. Meenakshi Rani  , Dr. Elturabi Osman Ahmed  , Dr. Yusuf Perwej  , S V Anil kumar  , Dr. Venkatesan Hariram  , Vani V G
Published: December 2024

The rapid integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) in healthcare has enabled significant advancements in patient care, diagnostics, and operational efficiency. This paper presents a comprehensive framework for leveraging IoT, AI, and ML in healthcare analytics, focusing on the role these technologies play in collecting, processing, and analyzing patient data for better decision-making and predictive insights. The framework highlights applications in real-time monitoring, diagnosis, personalized treatment, and predictive analytics. A discussion of the current challenges, opportunities, and practical implications for healthcare providers is also included.
Rhetoric Versus Reality: Christianity Exposed In Bama’s Karukku And Sangati
Authors: G. Brabu  , Dr. E. Anita
Published: December 2024

The caste system is a form of social stratification unique in Hindu society of India. It privileges the upper class and condemns the lower class, otherwise known as marginalized by creating a gap between them. To escape this oppression, some communities have got themselves converted to Christianity. In Karukku, it is evident that the community of parayas got converted to Christians but they are still subjected to caste-based discrimination. The paper titled “Rhetoric versus Reality: Christianity Exposed in Bama’s Works” attempts to prove that the institution of Christianity failed in rescuing the dalits from caste based discrimination. The paper further probed how Bama feels discriminated even inside the convent.
Comprehensive Survey on Object Detection and Anomaly Detection Techniques for Ensuring Pedestrian Safety in Walkways
Authors: Hima Bindu Gogineni  , Hemanta Kumar Bhuyan  , E Laxmi Lydia
Published: December 2024

Ensuring pedestrian safety in walkways gains more significance in urban surroundings. Anomaly detection (AD) approaches had a crucial role in assuring pedestrian safety in pathways, where abnormal events or behaviours can give rise to potential risks. To attain this, potential AD and object detection approaches are essential to identify abnormal behaviours and potential hazards in real-time. This survey presents a complete overview of AD and object detection algorithms particularly devised for pedestrian walkways. The survey starts by discussing the importance of pedestrian safety and the difficulties related to identifying anomalies and objects in walkway settings. Then it delivers a comprehensive analysis of different object detection algorithms that includes existing deep learning methods and classical computer vision (CV) based methods. The applications, strengths, and limitations of all methods are inspected, emphasizing their potentiality in pedestrian tracking and detection. Moreover, the survey explored AD approach that intends to determine abnormal events or behaviours in walkways. Such methods have machine learning-based methods, statistical modeling, and AD methods designed for pedestrian-centric situations. The survey inspects their respective pros, cons, and real-time applications. The survey concluded by discussing future directions and emerging trends in object detection and AD for pedestrian safety in walkways.
An Efficient Neuroimaging in Parkinson’s Disease Detection with A Hybrid Model Approach
Authors: Bangaru Lakshmi Mahanthi  , S Anuradha
Published: December 2024

Millions of people around the world have Parkinson's disease. This progressive neurogenerative disorder is characterized by many motor and non-motor symptoms, most notably tremors, bradykinesia, and stiffness. While there is no cure, ongoing research aims to enhance our understanding of the disease and develop more effective treatments. It has been identified that Parkinson’s disease is a fast-emerging disease and causes many mortality rates. The identification of the disease at the earlier stage minimized some of the challenges of Parkinson’s disease. In this area of research, an attempt is made to identify a framework that can quickly identify the disease at an earlier stage. Early and accurate determination of Parkinson's Disease (PD) is vital for effective intervention and management. This article presents a novel hybrid approach that coordinates Ada Boost with neural networks to recognize PD from other conditions utilizing MRI information. The inspiration stems from the squeezing need to improve diagnostic exactness in neurodegenerative diseases, especially Parkinson's. Leveraging a comprehensive dataset of MRI scans from PD patients and controls, our system dynamically alters weights and neural systems to handle the complex 3D nature of MRI data. Evaluation measurements, including accuracy, sensitivity, specificity, and ROC curve analysis, provide knowledge of system performance preliminary results and clinical results. The study contributes to the field of neurology by providing a novel method for early and accurate diagnosis of PD using MRI data. By integrating machine learning techniques with MRI analysis, our approach enhances diagnostic accuracy and offers the potential for early intervention, ultimately improving patient care and outcomes.
Chronic Kidney Disease Prediction using Hybrid Optimization Search Model (HOSM)
Authors: S. Mohammed Imran  , N. Prakash  , Arif Abdul Rahman
Published: December 2024

Kidney is an important organ of the human body helps to keep balancing the functionality of all other organs. Chronic kidney diseases(CKD)create many impacts in recent days due to changes in life style habits, lack of health awareness and irregular treatments on health issues. Diabetics is one of the largest impacted diseases affect the humans. A chronic diabetic allows slow degradation of organs health when untreated regularly. A chronic disease covers various health problems that is resilient in diagnosis. The proposed research work is aims to develop an comparative analysis model that deep dig the attributes of the chronic kidney dataset collected from Kaggle.com. the standard dataset act as the benchmark for initially creating the machine learning models. The novel framework acted upon with tuning the hyper parameters using hybrid optimization approach. Machine learning models such as Adaptive boosting model (ADAB), CatBoostAlgorithm (CA), K-Nearest neighbor algorithm (KNN), Probability boosted regression (PBR) and Wide – Deep decision tree model (WADET) etc. the comparative analysis on CKD is validated through output parameters in the form of prediction accuracy, system precision, Recall and F1 score. The novel system relies on hybrid optimization enabled search model represented as HOSM to find out the best adaptive approach on developed systems. The robust framework is iteratively performing the validation process thus provides N-Cross validation mechanism that adopt dynamic inputs. The presented system using HOSM-WADET achieved 98.77% cumulative accuracy comparing the state of art approaches.
Team Sports Coaching and Performance in a Government University in Wuhan Province, China
Authors:   Emilio Aguinaldo College, Hong Wang
Published: December 2024

This study will make assumptions about the relationship between transformational leadership, team performance and team cohesion on the basis of literature and conduct statistical analysis in combination with design scales to verify the assumptions. The study evaluated the team sports coaches' transformational leadership and team performance of selected team sports college from Wuhan Province, China. Using a quantitative research technique, the study is based on a descriptive-correlational design, and was conducted among 85 student athletes in Wuhan, China. Based on the study findings, the demographic profile of the student respondents revealed that majority of the student respondents are within the age range of 20 to 23 years old, are females in terms of sex, have been playing team sports for 3 to 4 years, and are involved with Basketball as their sports. The students' neutrality on the extent of coaches' transformational leadership, particularly in terms of individualized consideration, highlights the need for a more consistent and comprehensive application of transformative qualities. The neutrality of students regarding team performance, coupled with the occasional observation of team members' roles, suggests an opportunity for coaches to enhance team awareness and communication. Age significantly influences student athlete respondents' assessments of coaches’ transformational leadership, while sex, length of playing team sports, and team position do not significantly impact these assessments in terms of charismatic influence, inspirational motivation, individualized consideration, and intellectual stimulation. The correlation analysis suggests that the perceived leadership qualities of Charismatic Influence, Inspirational Motivation, Individualized Consideration, and Intellectual Stimulation have significant relationships with various aspects of employee retention, as indicated by respondents' assessments of team member roles, inter-group relationships, passion, commitment, skills, and overall worker morale.
Systematic Review of IFRS, FDI, Institutional Quality, and Economic Growth
Authors: AL-Tuwaijari Musatfa Rasim , Abdul Rahman Mara Ridhuan Bin Che , Ahmad Azlina , Abdul Rahim Ruzita
Published: December 2024

This article aims to review the work being done by scholars on the connection between FDI, as well as (IFRS), institutional quality, and economic growth literature by identifying the fundamental features of this connection and charting the research landscape that organizes the earlier work into a logical classification.The purpose of this literature review, which began in December 2023, was to provide a current and cutting-edge analysis of research on the connection of the relationship between (IFRS) and foreign direct investment (FDI). economic growth and institutional quality, as well as drawing attention to current research trends in this field. This methodology is different from other evaluations in that it focuses on assessing how IFRS adoption affects economic development by focusing on (FDI) as a mediating variable and how IFRS adoption affects the attractiveness of foreign direct investment.To find all articles about (IFRS), (FDI), the caliber of institutions, and economic growth, the survey searched four major databases: Scopus, Web of Science (WoS), Science Direct, and Emerald Insight. From 2014 to 2023, 3469 articles were found using the first search, which was conducted across 124 Scopus, 1481 Web of Science (WOS), 18 Science Direct, and 1846 Emerald Insight databases. These databases are sufficiently comprehensive to encompass the literature on (IFRS), FDI, and economic growth. 1887 articles were initially duplicated throughout the four databases. However, additional articles were eliminated in 1582 when titles and abstracts were scanned, leaving 223 studies in focus. Ultimately, 25 articles remain in the final included set after 198 articles are eliminated through full-text reading.The results of the literature review show that there is a lot of active and diverse research on the connection between (IFRS) adoption, Economic development, institutional integrity, and foreign direct investment; nevertheless, a more thorough examination of this link is required. For subsequent researchers to profit from this line of inquiry, this study believes that the survey has helped clarify the possibilities and gaps that are now accessible.
Option agreements
Authors:   Institute of Legislation and Legal Information at Ministry of Justice of Republic of Kazakhstanс, Nesterova Yelena
Published: December 2024

Abstract. The article ‘Option Agreements’ by O. V. Nesterova discusses the the concept and types of option agreements and their application, using historical, comparative and analytical methods of scientific research. The author analyses the Kazakhstani regulation of option agreements in the field of venture capital financing and stock trading. The author distinguishes option agreements from related legal structures: preliminary agreement, conditional transaction, subscription agreement, framework agreement, protocol (memorandum) of intent and agreement with open terms. Based on the practice of Kazakhstani courts, the article analyses the practical aspects of concluding and performing option agreements under Kazakhstani law.
Keywords: option, financial instrument, obligation, stock exchange transaction, venture capital financing, unnamed contract, option agreement, option to enter into a contract
Knowledge Representation in Expert Systems: Structure, Classification, and Applications
Authors: Sabzaliyev Ali
Published: December 2024

This study explores the foundational principles, classification, and applications of expert systems, focusing on knowledge representation, inference mechanisms, and the challenges of dynamic adaptability. It highlights future trends, including integration with machine learning, user-friendly interfaces, and real-time scalability, emphasizing the transformative potential of expert systems in solving complex real-world problems.
A ATUAÇÃO DO ENFERMEIRO INTENSIVISTA NAS COMPLICAÇÕES APRESENTADAS EM PACIENTES NA TERAPIA RENAL SUBSTITUTIVA: REVISÃO INTEGRATIVA
Authors: Castro Gustavo Pires de , Almeida Daniella Valença Daher de , Sousa Layane Cristine da Silva
Published: December 2024

A terapia renal substitutiva (TRS) é frequentemente empregada em unidades de terapia intensiva (UTIs) para o tratamento de pacientes gravemente doentes com lesão renal aguda (LRA). O enfermeiro de terapia intensiva tem uma função crucial na detecção antecipada de complicações relacionadas ao tratamento de reanimação cardiopulmonar, tais como hipotensão, hipocalemia, hipovolemia, infecções e mudanças hemodinâmicas, que podem prejudicar o prognóstico do paciente. A finalidade deste estudo, uma revisão integrativa da literatura, foi identificar as principais complicações de pacientes que foram submetidos ao TRS em Unidades de Terapia Intensiva e detalhar a função do enfermeiro na prevenção e gestão dessas complicações. A análise incluiu 17 estudos publicados entre 2012 e 2023 em bases de dados pertinentes, como PubMed , CINAHL, Elsevier, BVS, Scielo e Lilacs.
Enhancing Prostate Cancer Prognosis through Digital Pathology and Machine Learning: A Systematic Review and Meta-Analysis
Authors: Ogbonna Chidiebere T. , Ayankoya F. Y. , Kuyoro S. O.
Published: December 2024

Analyzing digital pathology scans with machine learning can significantly improve prostate cancer prognosis. In recent years, machine learning (ML) algorithms have shown impressive capabilities in automating Gleason grading and prostate cancer prognostication, addressing challenges such as inter-observer variability among pathologists. This study investigates the development and validation of machine learning models specifically designed for prostate cancer prognostication. The study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review includes studies conducted between 2010 and 2024 that applied machine learning techniques to analyze digital pathology scans for prostate cancer prognosis. Inclusion criteria were studies focused on machine learning, prostate cancer, and digital pathology or whole-slide scans in the context of prostate cancer prognosis. Exclusion criteria included studies not involving machine learning, digital pathology, or prostate cancer, as well as studies not published in English or outside the specified time frame. Convolutional Neural Networks (CNNs) were the most commonly used machine learning approach in the reviewed studies, with brief mentions of other techniques. Meta-analysis was conducted using GraphPad Prism to create graphical representations of machine learning techniques employed in prostate cancer prognosis. The findings underscore the transformative potential of combining machine learning with digital pathology in revolutionizing prostate cancer prognosis. The integration of deep learning algorithms with digital pathology scans offers more accurate and efficient prognostication, significantly improving patient outcomes in the fight against prostate cancer.
The Innovative Organization: Fostering a Culture of Continuous Learning and Experimentation
Authors: Westover Jonathan
Published: December 2024

Abstract: This article examines strategies for cultivating a culture of continuous innovation within organizations. As disruption accelerates, businesses must develop capabilities for ongoing learning and experimentation to thrive. Drawing from two decades of consulting and academic research, the article considers key factors that distinguish highly innovative cultures. These include visible leadership commitment to innovation through clear communication and risk-taking behaviors. An environment conducive to continuous learning through opportunities for skill development, collaboration, and small failures/wins is also examined. In addition, the brief explores how activities such as recognition and rewards for innovative behaviors, collaborative experimentation across silos, and cross-pollination of diverse perspectives can spur creative insights. Industry examples from companies such as Netflix, Amazon, Google, 3M, Pixar, LinkedIn, and Best Buy provide illustrations of effective practices. The article concludes by outlining approaches leaders can take to assess strengths and opportunities within their organizations to foster a mindset where breakthrough ideas routinely emerge and scale.
The Mediterranean Canthydrus Sharp and taxonomic notes on C. arabicus Sharp, 1882 (Coleoptera, Noteridae)
Authors: Toledo Mario E.
Published: December 2024

Two species of Canthydrus Sharp (Coleoptera, Noteridae) are recog nized to occur in the Mediterranean basin: the eastern C. diophthalmus (Reiche & Saulcy, 1855) from Egypt to Middle Orient, reaching Uzbekis tan, and the western Mediterranean C. siculus (Ragusa, 1882) stat. nov. previously treated as a junior synonym of C. diophthalmus, occurring in Egypt, Algeria, Morocco, Italy (Sicily and Sardinia), Spain (Andalusia and Baleares) and most likely Libya and Tunisia. Canthydrus diophthalmus is formally recorded also for some localities in sub-Saharan Africa, and spe cimens observed from Sudan confirm the occurrence of this species in the Afrotropical region, but it cannot be affirmed whether also C. siculus occurs in tropical Africa. The two species are very similar, but can be easily separated by aedeagal features and, often, by the extension of the yellow lateral markings of the pronotum. A third species, the strictly Afrotropical C. koppi Wehncke, 1883, is very closely related and together these species form the C. diophthalmus species complex. The three species are described and illustrations of habitus and male genitalia are given. Canthydrus or natus Sharp, 1882 is no longer treated as a junior subjective synonym of C. luctuosus (Aubé), but as a junior subjective synonym of C. diophthal mus (Reiche & Saulcy) (new synonymy). First records are presented for C. diophthalmus (Sudan, Uzbekistan) and C. koppi (Benin, Burkina Faso). Canthydrus arabicus Sharp, 1882 is redescribed on the basis of type mate rial and its habitus and male genitalia are illustrated for the first time. The species is considered closely related to C. notula (Erichson, 1843) the male genitalia of which are also illustrated for comparison.
Sosialisasi Kurikulum 2013, Kurikulum Merdeka Belajar dan RPP di SD IT Makmuniyyah Tanjung Pura Langkat
Authors: Sabariah Hayatun , Ridha Zaifatur , YM Khairudin
Published: December 2024

Kurikulum merupakan bagian terpenting pada bidang pendidikan di Indonesia, sebab pendidikan merupakan ujung tombak kemajuan suatu Negara. Pengabdian ini dilaksanakan karena masih adanya guru-guru yang belum memahami kurikulum 2013 dan membedakan kurikulum merdeka belajar, maka dengan adanya hal ini dosen Sekolah Tinggi Jamiyah Mahmudiyah Tanjung Pura menyelenggarakan pengabdian kepada masyarakat di Sekolah Dasar Islam Terpadu Makmuniyyah Tanjung Pura. Pada observasi awal ditemukan kegagapan guru dalam membedakan kurikulum 2013 dan kurikulum merdeka belajar dan pembuatan RPP yang terjadi saat wadah pandemic melanda di Indonesia. Metode yang dilakukan pada pengabdian ini adalah metode deskriptif kualitatif melalui studi literature dengan telaah buku, artikel, jurnal, dan sumber informasi lainnya yang relevan dengan kurikulum yang berlaku di Indonesia.
السلجماسي و كتابه المنزع البديع في تجنيس أساليب البديع
Authors: هني عبد القادر
Published: December 2024

إن المصادر القديمة لا تقدّم لنا معلومات وافية عن صاحب "المنزع" أبي القاسم، على الرّغم من أنّ مؤلّفه يعدّ من المصنّفات النّقدية القيّمة الّتي تزخر بها المكتبة العربية والمغربية؛ فلا نزال نجهل كثيرا من تفاصيل حياته. كما أنّنا لا نعرف شيئا ذا بال عن شيوخه الذين تلقّى عنهم علمه الغزير ، لكن ثمّة قرائن يمكنها أن تسعفنا في تسليط الضّوء على الرّجل، من ذلك فراغه سنة 704ه من تأليف كتابه"المنزع" الّذي أنجزه إملاء، وهو ما يوحي إلى أنّه كان حينها مكلّفا بتدريس علوم البلاغة. وتدلّ على أنّ قدمه رسخت في هذه العلوم، وأنّه كان في هذا المضمار من أعلام عصره، (عصر الدّولة المرينيّة) الّتي عرف أغلب حكامها بتشجيع العلوم، وبالسّهر على الارتقاء بالتعليم.
التحضيض في اللغة العربية وكيفية استخدامها في تراكيب اللغة العربية المختلفة
Authors: عبد الرحمن أحمد محمد خالد , عبد السلام محمد عبد الرحيم
Published: December 2024

تعتبر اللّغة العربيّة من أعظم الهِبات الّتي منّ بها المولى عزّ وجل على الإنسان، ووسيلة من أهمّ الوسائل الّتي حقّقت للإنسان الرّقيّ على مرّ الأزمان، فهي تحتلّ منذ نشأتها المكان الأهمّ في حياة الإنسان، لكونها أداته للاتّصال.
وتعنى اللّغة العربيّة بدلالة الألفاظ والمعنى المستقى منها، فلكلّ مفردة استخدام خاصّ ومعنى تؤدّيه. وقد حظيت اللّفظة بكونها عنصرا من عناصر النّظام اللّغوي باهتمام المشتغلين بالدّراسات  اللّغويّة قديما وحديثا، وهم الّذين سعوا نحو تعريفها وتحديد معالمها، ومن بينهم ابن عقيل.
ولهذا أتت هذه الورقة البحثيّة الموسومة بـ: "ظاهرة التّحضيض في اللّغة العربيّة تحمل بين ثناياها توظيف هذه الأدوات وأحكامها النّحوية، حتّى يتيسّر للقارئ فهم معانيها، وكيفية توظيفها في تعابير اللّغة العربيّة المختلفة. كما تهدف الورقة إلى صون اللّسان من اللّحن.
إطلالة تاريخية على معاجم اللغة العربية
Authors: سكران مجاور
Published: December 2024

يتناول المقال قضايا أساسية تخص المعجم العربي، خاصة ما يتعلق بأهميته ونشأته، كما يعرض أنواع المعاجم العربية بصورة إجمالية وتفصيلية من حيث مبانيها ومعانيها، ومن حيث كيفية ترتيب مداخلها، كما يطرح في الختام إشكالية إلمام المعجم العربي بسائر المفردات والألفاظ العربية، وهو ما يفتح الباب للإشارة إلى المعجم التاريخي للغة العربية.
حذف الفاعل في كتاب المنتخب والمختار لابن منظور دراسة في التركيب والدلالة
Authors: الخالدي بندر
Published: December 2024

الحذف ظاهرة لغويّة تشترك فيها اللّغات الإنسانيّة، وتبدو مظاهرها في بعض اللّغات أكثر وضوحا، ونحن نرى أنّ ثبات هذه الظاهرة في العربيّة ووضوحها يفوق غيرها من اللّغات لما جبلت عليه العربيّة في خصائصها الأصليّة من ميل إلى الإيجاز جعلها مثلا تضمر فعل الكينونة في الربط بين جزأي الجملة الاسميّة، ولا تذكر لفظا للتّعبير عن الكون المطلق أي مجرد الوجود، فهو واجب الحذف إذا كان خبرا للمبتدأ بعد لولا، أو خبرا لـ لا النافيّة للجنس، أو في غير ذلك من المواضع، وليس الأمر كذلك بالنسبة لكثير من اللّغات التي تظهر أفعال الكينونة.