System research and information technologies

Modeling cryptocurrency market dynamics using machine learning tools

Dmytro Martjanov, Yaroslav Vyklyuk, Mariya Fleychuk — Tue, 26 Dec 2023 00:00:00 +0200

The article analyzes the dynamics of the cryptocurrency market (Bitcoin) using econometric estimation tools based on machine learning models. The forecasting method is improved based on time series decomposition and lagged shifts of financial indicators. An ensemble of short-term forecast models for the Bitcoin exchange rate is built, and its accuracy is analyzed and compared to individual component models. Time series models are used along with calculated financial indicators (ADODS, NATR, TRANGE, ATR, OBV, RSI, ADTV). The absolute deviation of the short-term forecast amounted to $9.5, which is 0.06% of the absolute value.

Semi-supervised inverted file index approach for approximate nearest neighbor search

Anton Bazdyrev — Tue, 26 Dec 2023 00:00:00 +0200

This paper introduces a novel modification to the Inverted File (IVF) index approach for approximate nearest neighbor search, incorporating supervised learning techniques to enhance the efficacy of intermediate clustering and achieve more balanced cluster sizes. The proposed method involves creating clusters using a neural network by solving a task to classify query vectors into the same bucket as their corresponding nearest neighbor vectors in the original dataset. When combined with minimizing the standard deviation of the bucket sizes, the indexing process becomes more efficient and accurate during the approximate nearest neighbor search. Through empirical evaluation on a test dataset, we demonstrate that the proposed semi-supervised IVF index approach outperforms the industry-standard IVF implementation with fixed parameters, including the total number of clusters and the number of clusters allocated to queries. This novel approach has promising implications for enhancing nearest-neighbor search efficiency in high-dimensional datasets across various applications, including information retrieval, natural language search, recommendation systems, etc.

A multi-level decision-making framework for heart-related disease prediction and recommendation

Vedna Sharma, Surender Singh Samant — Tue, 26 Dec 2023 00:00:00 +0200

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.

Methodology of the countries’ economic development data analysis

Tue, 26 Dec 2023 00:00:00 +0200

The paper examines the issue of improving the methods of identification of economic objects and their analysis using algorithms of intelligent data processing. The use of the developed methodology in the economic analysis allows for improvement in the quality of management. It can be the basis for creating decision support systems to prevent potentially dangerous changes in the economic status of the research object. In this work, an improved method of c-means data clustering with agent-oriented modification is proposed, and a radial-basis neural network and its extension are proposed to determine whether the obtained clusters are relevant and to analyze the informativeness of state variables and obtain a subset of informative variables. The effect of applying data compression using an autoencoder on the accuracy of the methods is also considered. According to the results of testing of the developed methodology, it was proved that the probability of incorrect determination of the state was reduced when identifying the states of economic systems, and a reduced value of the error of the third kind was obtained when classifying the states of objects.

Blockchain transaction analysis: a comprehensive review of applications, tasks and methods

Yaroslaw Dorogyy, Vadym Kolisnichenko — Tue, 26 Dec 2023 00:00:00 +0200

Blockchain transaction analysis is a powerful tool to gain insights into the actions and conduct of participants within blockchain networks. This article aims to extensively examine the applications, tasks, and methods associated with blockchain transaction analysis. We look at various uses of transaction analysis, ranging from its instrumental role in blockchain development to its pivotal significance in the field of criminal investigations. By leveraging common techniques and technologies employed in conducting such an analysis, we unlock hidden insights and uncover information that is not visible at first look. This article offers a wide-ranging perspective on the profound significance of blockchain transaction analysis while shedding light on its key role within the cryptocurrency industry and its wide-ranging applications beyond.

Raising the information security awareness among social media users in the Middle East

Hend Khalid Alkahtani — Tue, 26 Dec 2023 00:00:00 +0200

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.

Novel modified kernel fuzzy c-means algorithm used for cotton leaf spot detection

Pradip Paithane, Sarita Jibhau Wagh — Tue, 26 Dec 2023 00:00:00 +0200

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.

Information system for assessing the informativeness of an epidemic process features

Tue, 26 Dec 2023 00:00:00 +0200

The primary objective of this study is to assess the informativeness of various parameters influencing epidemic processes utilizing the Shannon and Kullback–Leibler methods. These methods were selected based on their foundation in the principles of information theory and their extensive application in machine learning, statistics, and other relevant domains. A comparative analysis was performed between the results acquired from both methods, and an information system was designed to facilitate the uploading of data samples and the calculation of factor informativeness impacting the epidemic processes. The findings revealed that certain features, such as “Chronic lung disease,” “Chronic kidney disease,” and “Weakened immunity,” did not carry significant information for further analysis and hindered the forecasting process, as per the data set examined. The developed information system efficiently supports the assessment of feature informativeness, thereby aiding in the comprehensive analysis of epidemic processes and enabling the visualization of the results. This study contributes to the current body of knowledge by providing specific examples of applying the described algorithmic models, comparing various methods and their outcomes, and developing a supportive tool for analyzing epidemic processes.

Survey of image deduplication for cloud storage

Shilpa Chaudhari, Ramalingappa Aparna — Tue, 26 Dec 2023 00:00:00 +0200

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 five-dimensional taxonomy for analysing the features and performance with several non-overlapping 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.

A comprehensive survey on load balancing techniques for virtual machines

Suman Sansanwal, Nitin Jain — Tue, 26 Dec 2023 00:00:00 +0200

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.