International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
Not a member yet
459 research outputs found
Sort by
Building Resilient Security Systems with Identity Access Management or Identity Access Management as a Pillar of Cybersecurity in Organizations
Full text linkThis article examines the implementation of identity access management (IAM) systems as a key component in building resilient and secure cybersecurity infrastructures within organizations. The threats faced by digital systems necessitate strict access control to protect data and prevent cyberattacks. The objective of this study is to analyze the role of IAM systems in creating secure environments that ensure controlled access to information resources, mitigate data leakage risks, and maintain the stability and security of organizational infrastructure. The study presents theoretical aspects of IAM system functionality, reviews existing solutions, and analyzes approaches to integrating IAM with other security components, such as incident response systems,authoritative application and endpoint protection tools. The findings confirm that implementing IAM systems enhances protection against both external and internal threats. This is due to centralized access control, which minimizes the risk of human error. The materials in this article will be valuable to cybersecurity professionals, IT managers, and researchers working on improving data protection strategies. In conclusion, the study emphasizes that adopting IAM systems enables organizations to safeguard their information resources by creating architectural solutions capable of responding promptly to emerging threats
Creating Custom Animations Using Motionlayout in Android
Full text linkThis article discusses the process of creating custom animations using Motion Layout in Android. Motion Layout, as an extension of ConstraintLayout, provides developers with a powerful tool for managing animations and transitions between layouts. The main focus is on describing the features of Motion Layout, such as creating smooth transitions and complex animation effects with a minimum amount of code, as well as integration with various user interactions. An overview of the key components, including MotionScene and ConstraintSet, that provide flexibility and power in animation development is provided. Practical code examples and recommendations for using Motion Layout to improve the user interface of mobile applications are considered. The article also focuses on the relevance and demand for the use of animations in modern mobile applications, supporting this with statistical data on the growth of the mobile device market and user expectations
Comparative Analysis of Machine Learning Algorithms for Diabetes Prediction: Finding the Optimal Approach
Full text linkDiabetes, as a chronic disease, poses a rapidly escalating risk to human health, stemming from a complex interplay of factors such as obesity, elevated blood glucose levels, and various other triggers. Central to its onset is the disruption of insulin hormone function, resulting in abnormal metabolism and increased blood sugar levels. In this paper, we propose a solution to this pressing issue using machine learning techniques. By applying various machine learning algorithms on the Pima Indian diabetes (PID) dataset, we aim to identify the most effective algorithm for this task. Leveraging powerful machine learning algorithms such as (SVM) Support Vector Machine, (RF) Random Forest and others, we endeavor to forecast the onset of diabetes. Through the amalgamation of these techniques, our objective is to proactively identify individuals at risk, enabling timely intervention and preventive measures to safeguard health. The primary goal of this initiative is to mitigate the risk of diabetes onset by forecasting individuals\u27 susceptibility and advocating for lifestyle and dietary adjustments. This study has dual objectives: firstly, to develop and implement a predictive model for diabetes using machine learning techniques, and secondly, to explore effective strategies for achieving success in this endeavor
Multi-Class Cancer Classification with SVM Using Wrapper Forward and Backward Feature Selection for Dimension Reduction
Full text linkThe use of machine learning (ML) into healthcare has shown enormous growth in recent years. The efficacy of supervised ML models is significantly influenced by the quality of the training data. Feature selection is a crucial factor that affects the performance of machine learning models, especially in complex tasks like multi-class cancer classification. This research investigates the efficacy of using forward feature selection and backward feature elimination approaches in combination with logistic regression. The features generated using these approaches are then used for cancer type classification using support vector machines (SVM).The focus of our study is to use a partially complete gene dataset obtained from the Indian Council of Medical study (ICMR) for the purpose of classifying different types of cancer using Support Vector Machines (SVM). Our approach demonstrated a remarkable success rate of 96% when using features selected via the forward selection method and 97% when using features obtained through the backward selection method in multi-class cancer classification
The Role of Continuous Integration in Accelerating Development and Reducing Defect Risks
Full text linkContinuous Integration (CI) is a key practice in software development aimed at minimizing integration errors and speeding up the development process by regularly and frequently merging code changes into a common repository. The paper analyzes the methodological foundations of CI and demonstrates how systematic integration contributes to the early detection of defects and improves the quality of the final product. The technical aspects of CI implementation are considered, including the choice of tools, process configuration and organizational challenges. The authors emphasize the importance of CI in the context of adapting to rapidly changing market demands and technological innovations, discussing benefits such as improved team collaboration, reduced risks associated with late error detection, and faster time to market. In conclusion, it is emphasized that successful CI integration requires cultural changes in teams and approaches to project management
Development of an Optimized Keyboard for the Tamazight Language: Integration of the Letter Frequency Model for Improved Ergonomics and Efficiency
Full text linkThe need for an ergonomic keyboard layout to minimize strain on wrists and fingers during prolonged use has become increasingly important with the proliferation of digital devices. For languages without a standardized keyboard layout, such as Tamazight, makeshift solutions have been used, leading to discomfort and strain on the user\u27s hands and wrists, occasionally resulting in conditions such as repetitive strain injury (RSI) and tendonitis in the wrists. This study presents a novel approach to developing an optimized keyboard layout for Tamazight that focuses on user comfort and minimizes strain on the wrists and fingers. A keyboard stress model was developed in which the keys are classified according to their degree of difficulty and stress, taking into account the position of the keys and the strength and length of the fingers. A textual analysis of novels and songs with over 17,000 words in Tamazight was conducted to determine for the first time the frequency-letter model for the Tamazight language, mainly Kabyle. The frequency of use of each letter was used to distribute them based on the estimated stress level for each finger on the keyboard. The resulting layout minimizes the need for frequent finger switching and includes all the necessary additional keys for Tamazight language use, which is a significant improvement and a major step forward for the standardization of the Tamazight keyboard. The finished layout was implemented with the Microsoft Keyboard Layout Creator (MSKLC)
Architectural Solutions for Scaling SAP BI Systems
Full text link The article discusses architectural solutions for scaling SAP Business Intelligence (BI) systems. SAP BI is a set of tools designed for managing and analyzing large amounts of data, which allows organizations to obtain useful information to optimize operations. The main aspects of scaling SAP BI systems include architectural approaches, the use of various components such as SAP Business Objects, SAP BW and SAP HANA, as well as the introduction of flexible methods and tools to ensure the sustainability and performance of systems. The stages of development of BI solutions are discussed, starting from simple integrations to complex data storage and analytics systems. Examples of the use of various architectural solutions are provided, depending on the scale and needs of organizations, as well as recommendations for improving data quality and reducing the load on the source systems. The article highlights the importance of a systematic approach to data management and analysis in order to achieve long-term efficiency and competitiveness in the market
Comparative Analysis of the Performance of Machine Learning- Models in the Prediction of Credit Risk Assessment
Full text linkThis article conducts a comparative analysis of various machine learning models in predicting credit risk assessment. The study aims to discern the most effective model for enhancing accuracy and efficiency in this domain. Leveraging a comprehensive historical credit dataset with diverse borrower attributes and credit performance indicators, several machine learning algorithms, including K-Nearest Neighbors, decision trees, support vector machines, random forests, and Naive Bayes, were rigorously evaluated. Through meticulous data preprocessing and feature extraction techniques, the performance of each model was assessed using key evaluation metrics such as accuracy, precision, and recall. The findings highlight the superior predictive capabilities of certain models over others in identifying credit defaults and non-performing loans, shedding light on nuanced variable interactions influencing credit risk. This analysis serves as a valuable guide for financial institutions seeking to adopt the most effective machine learning model in their credit risk assessment processes
AIGOS: Adversarial Interference for Generation Optimization System for Enhanced Synthesis and Robustness in Visual Content Creation
Full text linkThis research presents AIGOS (Adversarial Interference for Generation Optimization), an innovative framework designed to enhance image synthesis through a re-engineered Generative Adversarial Network (GAN) architecture. AIGOS uniquely positions the training dataset as the discriminator, enabling a process termed super-validation. This approach allows the generator to produce images that closely mimic real samples by receiving direct feedback from the dataset, thus optimizing its outputs based on the underlying data distribution. The framework emphasizes iterative refinement driven by adversarial loss, which significantly improves image quality and fidelity. By leveraging advanced techniques such as Low-Rank Adaptation (LoRA), AIGOS fine-tunes pre-trained models efficiently, minimizing overfitting while maximizing adaptability. Furthermore, AIGOS incorporates adversarial interference, introducing controlled perturbations during training to challenge the generator and enhance its resilience against distortions. Additionally, the integration of OpenCLIP, a multimodal model for similarity computation, facilitates perceptual alignment between generated images and their real counterparts, further elevating image quality. The methodology promotes rapid prototyping and effective feature learning, thereby improving collaboration among stakeholders and fostering innovation in blueprint generation. Ultimately, AIGOS establishes a comprehensive methodology for high-performance image generation systems, significantly advancing the field of generative modeling in visual content creation
Low Dimension Medical Images and Generative Deep Learning Models Can Help to Reduce X-Ray Radiation Exposure of Patients
Full text linkBackground: X-ray and other radiation-based diagnosis form a critical step in many clinical investigations, including early detection of diseases. Deep learning based methods to derive diagnosis from medical images have been shown to be highly accurate in this regard. However, the radiological images collected for this purpose continue to be guided by what is suitable for clinical practitioners to visually interpret them, ignoring the possibility that machines can detect patterns better than the human eye, making the high dimension images unnecessary. On the other hand, image analysis studies have primarily focused on classification accuracy, ignoring the diagnostic tradeoffs with radiation exposure.
Methods: Chest X-ray images from medical datasets have been modeled using EfficientNetB0 deep learning model by reducing the images to different pixel sizes: 1 x 1, 2 x 2, 4 x 4, 8 x 8, 16 x 16, 32 x 32, 64 x 64, 128 x 128, 224 x 224, 256 x 256 and 300 x 300 pixels. The effect of increasing image size on the predictive power of the model has been studied viz-a-viz the radiation exposure of the patient for collecting a chest X-ray image of that size.
Results: In this work, we show that reduced image sizes from the original X-ray images are capable of accurate diagnosis of medical conditions with little loss in predictive power and propose that potentially lower dimensions than what is needed for visual inspection may be sufficient for the purpose, thereby substantially reducing the risks associated with high radiation dosage, currently practiced for use of images by human interpretation. We also demonstrate how reduced images can be used to generate high dimension versions suitable for visual inspection with the help of generative super-resolution techniques (SRGAN) based on deep learning.
Conclusions: In summary this paper makes a case for low dimension collection of X-ray images, with accurate clinical outcomes and thereby addresses the issue of resolution versus diagnostic accuracy