Системні дослідження та інформаційні технології: міжнародний науково-технічний журнал, № 4

Постійне посилання зібрання

https://ela.kpi.ua/handle/123456789/65559

Переглянути

Перегляд Системні дослідження та інформаційні технології: міжнародний науково-технічний журнал, № 4 за Ключові слова "62-50"

Зараз показуємо 1 - 5 з 5
  • Ескіз
    ДокументВідкритий доступ
    A comprehensive survey on load balancing techniques for virtual machines
    (КПІ ім. Ігоря Сікорського, 2023) Suman Sansanwal; Nitin Jain
    Cloud computing is an emerging technique with remarkable features such as scalability, high flexibility, and reliability. Since this field is growing exponentially, more users are attracted to fast and better service. Virtual Machine (VM) allocation plays a crucial role in cloud computing optimization; hence, resource distribution is not impacted by machine failure and is migrated with no downtime. Therefore, effective management of virtual machines is necessary for increasing profit, energy-saving, etc. However, it could utilize the virtual machine resources more efficiently because of the increased load, so load balancing is more concentrated. The predominant purpose of load balancing is to balance the available load equally among the nodes to avoid overloading or underloading problems. The present study conducted an extensive survey on virtual machine placement to describe the application of prediction algorithms and to provide more efficient, reliable, high response, and low overhead VM placement. Furthermore, the survey attempted to overview the challenges in load balancing in VM placement and various ideas of state-of-the-art techniques to resolve the issues.
  • Ескіз
    ДокументВідкритий доступ
    A multi-level decision-making framework for heart-related disease prediction and recommendation
    (КПІ ім. Ігоря Сікорського, 2023) Vedna Sharma; Surender Singh Samant
    The precise prediction of health-related issues is a significant challenge in healthcare, with heart-related diseases posing a particularly threatening global health problem. Accurate prediction and recommendation for heart-related diseases are crucial for timely and effective treatment solutions. The primary objective of this study is to develop a classification model capable of accurately identifying heart diseases and providing appropriate recommendations for patients. The proposed system utilizes a multilevel-based classification mechanism employing Support Vector Machines. It aims to categorize heart diseases by analyzing patient’s vital parameters. The performance of the proposed model was evaluated by testing it on a dataset containing patient records. The generated recommendations are based on a comprehensive assessment of the severity of clinical features exhibited by patients, including estimating the associated risk of both clinical features and the disease itself. The predictions were evaluated using three metrics: accuracy, specificity, and the receiver operating characteristic curve. The proposed Multilevel Support Vector Machine (MSVM) classification model achieved an accuracy rate of 94.09% in detecting the severity of heart disease. This makes it a valuable tool in the medical field for providing timely diagnosis and treatment recommendations. The proposed model presents a promising approach for accurately predicting heart-related diseases and highlights the potential of soft computing techniques in healthcare. Future research could focus on further enhancing the proposed model’s accuracy and applicability.
  • Ескіз
    ДокументВідкритий доступ
    Novel modified kernel Fuzzy C-Means algorithm used for cotton leaf spot detection
    (КПІ ім. Ігоря Сікорського, 2023) Paithane, Pradip M.; Sarita Jibhau Wagh
    . Image segmentation is a significant and difficult subject that is a prerequisite for both basic image analysis and sophisticated picture interpretation. In image analysis, picture segmentation is crucial. Several different applications, including those related to medicine, facial identification, Cotton disease diagnosis, and map object detection, benefit from image segmentation. In order to segment images, the clustering approach is used. The two types of clustering algorithms are Crisp and Fuzzy. Crisp clustering is superior to fuzzy clustering. Fuzzy clustering uses the well-known FCM approach to enhance the results of picture segmentation. KFCM technique for image segmentation can be utilized to overcome FCM’s shortcomings in noisy and nonlinear separable images. In the KFCM approach, the Gaussian kernel function transforms high-dimensional, nonlinearly separable data into linearly separable data before applying FCM to the data. KFCM is enhancing noisy picture segmentation results. KFCM increases the accuracy rate but ignores neighboring pixels. The Modified Kernel Fuzzy C-Means approach is employed to get over this problem. The NMKFCM approach enhances picture segmentation results by including neighboring pixel information into the objective function. This suggested technique is used to find “blackarm” spots on cotton leaves. A fungal leaf disease called “blackarm” leaf spot results in brown leaves with purple borders. The bacterium can harm cotton plants, causing angular leaf blotches that range in color from red to brown.
  • Ескіз
    ДокументВідкритий доступ
    Raising the information security awareness among social media users in the Middle East
    (КПІ ім. Ігоря Сікорського, 2023) Hend Khalid Alkahtani
    Social media presents both opportunities and risks for any firm. The Internet has recently made everything possible. Due to its low cost and rapid speed, it is in high demand. Due to the virtual technique of interacting through various social media apps like Instagram, WhatsApp, Twitter, Facebook, etc., people are drawn to social networking. Despite the fact that it offers advantages on both sides, new threats are constantly emerging. Social media usage is widespread, but awareness is low, which makes significant cyberattacks more likely. Numerous threat categories put consumers at risk for cyber security. This research reviewed literature on educating Middle Eastern social media users about information security. Additionally, this research examines various threats made via social media, offers countermeasures, and considers various detection methods.
  • Ескіз
    ДокументВідкритий доступ
    Survey of image deduplication for cloud storage
    (КПІ ім. Ігоря Сікорського, 2023) Chaudhari, Shilpa; Aparna, Ramalingappa
    Increased growth of real-life communication has motivated the creation, transmission, and digital storage of vast volumes of images and video data on the cloud. The explosive increase in virtual/visual image data on cloud servers requires efficient storage utilization that can be addressed using image deduplication technology. Even though the virtual and visual image properties are different, the existing literature uses a similar approach for deduplication checks, which motivated us to consider both image types for this review. This article aims to provide a detailed survey of state-of-the-art visuals as well as virtual image deduplication techniques in a cloud environment, summarizing and organizing them by developing a fivedimensional taxonomy for analysing the features and performance with several nonoverlapping categories in each dimension. These include: 1) location of applying deduplication; 2) image feature extraction; 3) time of application; 4) image data partitioning strategy; 5) involvement of user dataset level. Existing image deduplication techniques are categorized into two main categories based on whether the technique involves security. A comparison of techniques is discussed across a set of functional and performance parameters. The current issues are highlighted with the possible future directions to motivate further research studies on the topic.