Volume 21 Issue 6
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Recently, pre-trained convolutional neural network CNNs have been widely used and applied for medical image classification. These models can utilised in three different ways, for feature extraction, to use the architecture of the pre-trained model and to train some layers while freezing others. In this study, the ResNet18 pre-trained CNNs model is used for feature extraction, followed by the support vector machine for multiple classes to classify medical images from multi-classes, which is used as the main classifier. Our proposed classification method was implemented on Kvasir and PH2 medical image datasets. The overall accuracy was 93.38% and 91.67% for Kvasir and PH2 datasets, respectively. The classification results and performance of our proposed method outperformed some of the related similar methods in this area of study.
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Yovenko, Larysa;Novakivska, Lyudmyla;Sanivskyi, Oleksandr;Sherman, Mykhailo;Vysochan, Lesia;Hnedko, Natalia 7
The article analyzes and concretizes the understanding of the differences between the concepts of competence / competence according to the criterion general - personal. Based on the identified characteristics of competence (completed personal quality, activity character, educational result, successful implementation of professional and educational activities), the concept of competence as an integrative dynamic quality of a person, manifested in effective activity in a specific area, is defined. The structure of the IC has been substantiated, including motivational and value; information technology; communicative and reflective components. The content of the named IC components is disclosed. The article analyzes the essence of the characteristics of basic concepts (competence / competence), consideration of information competence in the research of famous scientists in order to concretize the studied phenomenon; concretization of the identified pedagogical conditions in educational process. -
Ilanchezhian, P;Shanmugaraja, P;Thangaraj, K;Aldo Stalin, JL;Vasanthi, S 11
At the present time, the number of accidents has enlarged speedily and in country like India per day there are about 204 accidents occurred. Accidents of two-wheeler compose a foremost segment of every accident and it can be true for the reason that two-wheelers like bikes not able to produce as many as security measurements normally incorporated in cars, truks and bus etc. General main rootcost of the two-wheeler accidents happen only when people community not remember to wearing a device helmet and during the driving time feels like sleep condition, alcohol disbursement, many of the drivers doesn't know heavy vehicles like Loory and buses approaching into very closer to their two wheelers, contravention of two wheelers in traffic rules and regulations. Let's overcome the above situations; our important objective is to develop an intelligent system device that can successfully facilitate in avoidance of every kind of problems. Suppose any of the above stated situations occurs, at that moment how system device identify and represents the commanders and community, and finally the stated situation be able to taken care of straight away without any further delay. A smart intelligent helmet system is a defending head covering used by rider for making bike riding safer than earlier. This is finished by incorporating sophisticated features like detecting the usage of helmet by the rider, connected Bluetooth module in helmet. In order to maintain the temperature inside the helmet device we need to include CPU fan module inside the device. RF based helmet prevents road accidents and identify whether people community is not using a component helmet or used. Main responsibility of the system is to detect accidents by vibration sensors, accelerometers and also with the help of modules global positioning system and global system for mobile commnicaiton module. A wireless communication device used to discover the accident area site location and likewise notifying the two-wheeler drived people's relatives and short message text information passed to the positioned hospitals. -
The attack trend on end-users via mobile devices is increasing in both the danger level and the number of attacks. Especially, mobile devices using the Android operating system are being recognized as increasingly being exploited and attacked strongly. In addition, one of the recent attack methods on the Android operating system is to take advantage of Android Package Kit (APK) files. Therefore, the problem of early detecting and warning attacks on mobile devices using the Android operating system through the APK file is very necessary today. This paper proposes to use the method of analyzing abnormal behavior of APK files and use it as a basis to conclude about signs of malware attacking the Android operating system. In order to achieve this purpose, we propose 2 main tasks: i) analyzing and extracting abnormal behavior of APK files; ii) detecting malware in APK files based on behavior analysis techniques using machine learning or deep learning algorithms. The difference between our research and other related studies is that instead of focusing on analyzing and extracting typical features of APK files, we will try to analyze and enumerate all the features of the APK file as the basis for classifying malicious APK files and clean APK files.
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Surzhik, Dmitry I.;Kuzichkin, Oleg R.;Vasilyev, Gleb S. 23
The article discusses the features of adaptation of the parameters of the physical layer of data transmission in self-organizing networks based on unmanned aerial vehicles operating in the conditions of "smart cities". The concept of cities of this type is defined, the historical path of formation, the current state and prospects for further development in the aspect of transition to "smart cities" of the third generation are shown. Cities of this type are aimed at providing more comfortable and safe living conditions for citizens and autonomous automated work of all components of the urban economy. The perspective of the development of urban mobile automated technical means of infocommunications is shown, one of the leading directions of which is the creation and active use of wireless self-organizing networks based on unmanned aerial vehicles. The advantages of using small-sized unmanned aerial vehicles for organizing networks of this type are considered, as well as the range of tasks to be solved in the conditions of modern "smart cities". It is shown that for the transition to self-organizing networks in the conditions of "smart cities" of the third generation, it is necessary to ensure the adaptation of various levels of OSI network models to dynamically changing operating conditions, which is especially important for the physical layer. To maintain an acceptable level of the value of the bit error probability when transmitting command and telemetry data, it is proposed to adaptively change the coding rate depending on the signal-to-noise ratio at the receiver input (or on the number of channel decoder errors), and when transmitting payload data, it is also proposed to adaptively change the coding rate together with the choice of modulation methods that differ in energy and spectral efficiency. As options for the practical implementation of these solutions, it is proposed to use an approach based on the principles of neuro-fuzzy control, for which examples of determining the boundaries of theoretically achievable efficiency are given. -
Martynyshyn, Yaroslav;Kukin, Igor;Khlystun, Olena;Zrybnieva, Iryna;Pidlisnyi, Yevhen 29
The article formulates the key characteristics and features of country models of corporate governance. It was revealed that all countries are characterized by a fairly high concentration of ownership, insider control; Key gaps in the implementation of corporate governance principles were found: transparency and disclosure of information, protection of shareholders' rights, gender diversity of boards of directors, implementation of recommendations on the share of independent directors; The criterion of countries' efficiency (total investments) was identified and recommendations for their improvement were developed. -
Small and Medium Enterprises (SMEs) are increasingly using ERP systems to connect and manage all their functions, whether internally between the different departments, or externally with customers in electronic commerce. However, the selection of the right ERP system is usually an issue, due to the complexities of identifying the criteria, weighting them, and selecting the best system and provider. Because cost is usually important for SMEs, ERP systems based on Cloud Software as a Service (SaaS) has been adopted by many SMEs. However, SMEs face an issue of selecting the right system. Therefore, this paper proposes a fuzziness ranking engine system in order to match the SMEs requirements with the most suitable service provider. The extensive experimental result shows that our approach has better result compared with traditional approaches.
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The article deals with the problems of application of microwave methods in systems of geoecological monitoring of natural environments and resources of the agro-industrial complex. It is noted that the methods of microwave radiometry make it possible, by the power of the measured intrinsic radio-thermal radiation of the atmosphere, when solving inverse problems using empirical and semi-empirical models, to determine such parameters of the atmosphere as thermodynamic temperature, humidity, water content, moisture content, precipitation intensity, and the presence of different fractions of clouds.In addition to assessing the meteorological parameters of the atmosphere and the geophysical parameters of the underlying surface based on the data of microwave radiometric measurements, it is possible to promptly detect and study pollution of both the atmosphere and the earth's surface. A technique has been developed for the analysis of sources of measurement error and their numerical evaluation, because they have a significant effect on the accuracy of solving inverse problems of reconstructing the values of the physical parameters of the probed media.To analyze the degree of influence of the limited spatial selectivity of the antenna of the microwave radiometric system on the measurement error, we calculated the relative measurement error of the ratio of radio brightness contrasts in two angular directions. It has been determined that in the system of geoecological monitoring of natural environments, the effect of background noise is maximal with small changes in the radiobrightness temperature during angular scanning and high sensitivity of the receiving equipment.
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Monte-Carlo GO is a computer GO program that is sufficiently competent without using knowledge expressions of IGO. Although it is computationally intensive, the computational complexity can be reduced by properly pruning the IGO game tree. Here, I achieve this by using a potential model based on the knowledge expressions of IGO. The potential model treats GO stones as potentials. A specific potential distribution on the GO board results from a unique arrangement of stones on the board. Pruning using the potential model categorizes legal moves into effective and ineffective moves in accordance with the potential threshold. Here, certain pruning strategies based on potentials and potential gradients are experimentally evaluated. For different-sized boards, including an official-sized board, the effects of pruning strategies are evaluated in terms of their robustness. I successfully demonstrate pruning using a potential model to reduce the computational complexity of GO as well as the robustness of this effect across different-sized boards.
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Samiilenko, Halyna;Khudolei, Veronika;Kharchenko, Yuliia;Povna, Svitlana;Samoilovych, Anastasiia;Khanin, Semen 61
Within the article, forms of innovation structures are systematized, and those that exist in Ukraine are identified, their features, purpose and functions are clarified, activities and current state of their functioning are analyzed. It is proved that in the conditions of digital economy, domestic innovation structures are underdeveloped and do not fulfill their leading role at the present stage of innovative development of the country. To understand the importance of effective operation of innovation infrastructure, the main world models of innovation development are considered, and the directions of using the experience of their functioning for Ukraine are determined. The main indicators of innovation activity of Ukraine in the context of regions are analyzed. There is a significant differentiation of innovation indicators in the regional context. The cartographic analysis of innovative activity of regions is presented. The mechanism of definition and implementation of priority directions of innovative activity in the context of the digital economy development is offered. -
Barila, Adina;Danubianu, Mirela;Paraschiv, Andrei Marcel 71
Education is one of the pillars of sustainable development. For this reason, the discovery of useful information in its process of adaptation to new challenges is treated with care. This paper aims to present the initiation of a process of exploring the data collected from the results obtained by Romanian students at the BBaccalaureate (the Romanian high school graduation) exam, through data mining methods, in order to try an in-depth analysis to find and remedy some of the causes that lead to unsatisfactory results. Specifically, a set of public data was collected from the website of the Ministry of Education, on which several classification methods were tested in order to find the most efficient modeling algorithm. It is the first time that this type of data is subjected to such interests. -
Antar, Rayana Khaled;ALotaibi, Shouq Talal;AlGhamdi, Manal 77
Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks. -
Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.
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Alshanbari, Eman;Alamri, Hanaa;Alzahrani, Walaa;Alghamdi, Manal 101
Breast cancer is the number one cause of deaths from cancer in women, knowing the type of breast cancer in the early stages can help us to prevent the dangers of the next stage. The performance of the deep learning depends on large number of labeled data, this paper presented convolutional neural network for classification breast cancer from images to benign or malignant. our network contains 11 layers and ends with softmax for the output, the experiments result using public BreakHis dataset, and the proposed methods outperformed the state-of-the-art methods. -
Tufail, Ali;Namoun, Abdallah;Alrehaili, Ahmed;Ali, Arshad 107
The deployment of 5G is in full swing, with a significant yearly growth in the data traffic expected to reach 26% by the year and data consumption to reach 122 EB per month by 2022 [10]. In parallel, the idea of smart cities has been implemented by various governments and private organizations. One of the main objectives of 5G deployment is to help develop and realize smart cities. 5G can support the enhanced data delivery requirements and the mass connection requirements of a smart city environment. However, for specific high-demanding applications like tactile Internet, transportation, and augmented reality, the cloud-based 5G infrastructure cannot deliver the required quality of services. We suggest using multi-access edge computing (MEC) technology for smart cities' environments to provide the necessary support. In cloud computing, the dependency on a central server for computation and storage adds extra cost in terms of higher latency. We present a few scenarios to demonstrate how the MEC, with its distributed architecture and closer proximity to the end nodes can significantly improve the quality of services by reducing the latency. This paper has surveyed the existing work in MEC for 5G and highlights various challenges and opportunities. Moreover, we propose a unique framework based on the use of MEC for 5G in a smart city environment. This framework works at multiple levels, where each level has its own defined functionalities. The proposed framework uses the MEC and introduces edge-sub levels to keep the computing infrastructure much closer to the end nodes. -
The Internet of Things (IoT) applications are quickly spread in many fields. Blockchain methods (BC), defined as a distributed sharing mechanism, offer excellent support for IoT evolution. The BC provides a secure way for communication between IoT devices. However, the IoT environments are threatened by hacker attacks and malicious intrusions. The IoT applications security are faced with three challenges: intrusions and attacks detection, secure communication, and compressed storage information. This paper proposed a system based on double-blockchain to improve the communication transactions' safety and enhance the information compression method for the stored data. Information security is enhanced by using an Ellipse Curve Cryptography (ECC) considered in a double-blockchain case. The data compression is ensured by the Compressed Sensing (CS) method. The conducted experimentation reveals that the proposed method is more accurate in security and storage performance than previous related works.
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In the systems and software modeling field, a conceptual model involves modeling with concepts to support development and design. An example of a conceptual model is a description developed using the Unified Modeling Language (UML). UML uses a model multiplicity formulation approach, wherein a number of models are used to represent alternative views. By contrast, a model singularity approach uses only a single integrated model. Each of these styles of modeling has its strengths and weaknesses. This paper introduces a partial solution to the issue of multiplicity vs. singularity in modeling by adopting UML use cases and class models into the conceptual thinging machine (TM) model. To apply use cases, we adopt the observation that a use-case diagram is a description that shows the internal structure of the part of the system represented by the use case in addition to being useful to people outside of the system. Additionally, the UML class diagram is recast in TM representation. Accordingly, we develop a TMUML model that embraces the TM specification of the UML class diagram and the internal structure extracted from the UML use case. TMUML modeling introduces some of the advantages that have made UML a popular modeling language to TM modeling. At the same time, this approach supplies UML with partial model singularity. The paper details experimentation with TMUML using examples from the literature. Our results indicate that mixing UML with other models could be a viable approach.
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Altalhi, Amjaad;AL-Saedi, Maram;Alsuwat, Hatim;Alsuwat, Emad 137
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. -
Khan, Sadia;Fahiem, Muhammad Abuzar;Bakhtawar, Birra;Aftab, Shabib;Ahmad, Munir;Aziz, Nauman;Almotilag, Abdullah;Elmitwally, Nouh Sabri 143
Process model is an integral part of software industry. Different process models are used now a days in the industry for different software projects. Process models need to be tailored to address some specific project needs. Agile models are considered as the most widely used process models nowadays. They have distinctive features and the ability to address the dynamic needs of today's software development. Extreme programming (XP) is one of the extensively used agile process model especially for small projects. Many researchers have tried to mold XP to overcome its shortcomings and for better working in specific scenarios. Therefore, many customized versions of XP process model are available today. In this paper, we are going to analyze the latest customizations of XP. For this purpose, a systematic literature review is conducted on studies published from 2012 till 2018 in renowned online search libraries. This comprehensive review highlights the purpose of customizations, along with the areas in which customizations are made, and phases & practices which are being customized. This work will serve the researchers to discover the modern versions of XP process model as well as will provide a baseline for future directions for customizations. -
Marchenko, Olga;Noskova, Margaryta;Fedorenko, Igor;Semenog, Olena;Vovk, Myroslava;Romanyshyn, Ruslana 151
The article is based on a model, in the context of which there are two fundamental building blocks of basic library skills and skills in the use of information technology. The former are formed within the framework of educational programs for users of academic libraries, the latter are formed within the framework of initiatives such as the European Computer Driving License. Between the basic and the highest levels of the concept of "information literacy" there are seven heading skills and attributes, the repeated practice of which leads from the position of a competent user to an expert level of reflection and critical awareness of information as an intellectual resource. Freshmen will likely be at the beginning of the arrow, probably practicing only the first four skills, while graduate students and young scientists will be closer to the end and will use seven skills. -
Alotaibi, Leena;Alsalmi, Azhar;Alsuwat, Hatim;Alsuwat, Emad 156
Every day brings a new challenge to the humanities. Life nowadays needs accuracy, privacy, integrity, authenticity, and security to run life systems especially the monetary system. Things now differ from previous centuries. Multiple varieties in digital banking have opened the new and most advanced innovations for human beings. The monetary system is going to developed day by day to facilitate the public. Electronic money has amazed the world and gave a challenge to central banking. For this purpose, there will be a need for strict security, information, and confidence. Blockchain technology has opened new gateways. Bitcoin has become the most famous digital currency, which has created a thunderstorm in digital marketing. Blockchain, as a new Financial Technology, has satisfied all the security issues and satisfied doing business in secure ways that encourage investors to invest and keep the world business wheel. Assessment of the sustainability of implementing Bitcoin in financial institutions will be discussed. Every new system has its pros and cons in which a clear vision of what we are about to use can be sought. Through this research paper, a demonstration of the monetary system evolution, the new ways of doing business, some evidence in a form of academic cases will be demonstrated through comparison a table, a suggested method to transfer to the new system in safe mode will be proposed, and a conclusion will be concluded. -
Tulchynska, Svitlana;Vovk, Olha;Popelo, Olha;Saloid, Stanislav;Kostiunik, Olena 161
Within the article, strategic guidelines for the modernization of microeconomic systems are identified. Modernization levels of the potential implementation are formalized for enterprises: contractile, extensive technical, technological, progressive, adaptive, steady, intensive, creative, absolute and leader modernization. This allowed to specify the directions and tasks of the enterprise modernization at different management levels. Accordingly, the conditions and criteria for selecting resource tools are set. It is proved that the strategies of the potential modernization of enterprises must be carried out at four main management levels: first, at the enterprise level; secondly, for a particular type of product / service; third, by functional directions of modernization of separate spheres of the enterprise activity or responsibility, fourth, at the level of structural units of the enterprise. It is substantiated that in the processes due to the activation of the potential modernization, the resources are transformed into the results of the innovation implementation and the investment strategies modernization. A system of tasks for the corporate strategies implementation in order to modernize microeconomic systems has been formed. Key vectors of the activation determine the nature and properties of investment resources and necessary innovations to enhance the modernization potential. Therefore, the system of innovation and investment strategies' modernization, based on the vector and resource provision of the modernization process, is specified: -
Huong, Truong Thu;Bac, Ta Phuong;Thang, Bui Doan;Long, Dao Minh;Quang, Le Anh;Dan, Nguyen Minh;Hoang, Nguyen Viet 169
Since machine learning was invented, there have been many different machine learning-based algorithms, from shallow learning to deep learning models, that provide solutions to the classification tasks. But then it poses a problem in choosing a suitable classification algorithm that can improve the classification/detection efficiency for a certain network context. With that comes whether an algorithm provides good performance, why it works in some problems and not in others. In this paper, we present a data-centric analysis to provide a way for selecting a suitable classification algorithm. This data-centric approach is a new viewpoint in exploring relationships between classification performance and facts and figures of data sets. -
Pravdiuk, Andrey;Gerasymenko, Larysa;Tykhonova, Olena 181
Ensuring national security in cyberspace is becoming an increasingly important issue, given the growing number of cybercrimes due to adaptation to new security and protection technologies. The purpose of this article is to study the features of counteracting, preventing, and detecting crimes in the virtual space of Ukraine on the example of cases and analysis of the State Center for Cyber Defense and Countering Cyber Threats CERT-UA and the Cyber Police Department of the National Police of Ukraine. The research methodology is based on the method of analysis and study of cases of crime detection in the virtual environment of the State Center for Cyber Defense and Countering Cyber Threats CERT-UA and the Cyber Police Department of the National Police of Ukraine. The results show that the consistent development of the legal framework in 2016-2020 and the development of a cyber-defense strategy for 2021-2025 had a positive impact on the institution-building and detection of cybercrime in Ukraine. Establishing cooperation with developed countries (USA) has helped to combat cybercrime by facilitating investigations by US law enforcement agencies. This means that international experience is effective for developing countries as a way to quickly understand the threats and risks of cybercrime. In Ukraine, the main number of incidents concerns the distribution of malicious software in the public sector. In the private sector, cyber police are largely confronted with the misappropriation of citizens' income through Internet technology. The practical value of this study is to systematize the experience of overcoming cybercrime on the example of cases of crime detection in a virtual environment. -
The research aims to measure the control procedures' effectiveness, followed by the University of Northern Borders employees. A questionnaire was developed and distributed to the target sample of financial and auditing affairs employees at the university, where the researcher followed the existing descriptive-analytical approach. The researcher relied on the field survey, and statistical analysis (spss) was used. The researcher has found that the control procedures used are highly efficient in reducing public money waste. The researcher has presented recommendations that may contribute to developing the work of oversight in combating waste of public money. These recommendations include: Increase the interaction between the General Oversight Office and the internal oversight departments at the University of Northern Borders, the incentives provided to the oversight and accounting staff for their efforts to combat public money waste. It encourages them to maintain public money and work to obliging employees to undertake training courses periodically to develop their skills and rehabilitate them in line with modern control procedures. Also, more studies and scientific research on the waste of public money and types of administrative and financial Corruption and the law in all state sectors and reach conclusions and recommendations will help decision-makers amend laws and regulations to serve the public benefit of the university and the state.
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Alqethami, Sara;Almutanni, Badriah;AlGhamdi, Manal 200
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. -
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.
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Koval, Kristina;Hrechko, Alla;Butko, Mykola;Shevchenko, Oleg;Smyrnov, Ivan;Olyfirenko, Yuliia 213
The modernization approach to monitoring regional processes of providing social services is proposed. The methodological approach is developed in the context of the modern public policy, which includes the following stages: the formation of a system of indicators that characterize the level of the social services modernization; the determination of the levels of regions development by such components as the modernization level of the economic sphere, the modernization level of the demographic component and the modernization level of employment; the determination of weights for each group of indicators and calculation of integrated indicators, ranking of regions; the allocation of criteria for classification and grouping of regions according to the modernization level of the social services sphere; highlighting the most important problems of each region. The proposed method is tested on the example of the Ukraine's regions. According to the results of calculations of the modernization level of the processes of providing social services, the ranking and grouping of the regions was carried out. The rating allowed to distinguish four groups of regions: regions with a high modernization level of social services, regions with above-average levels, as well as regions with medium and low levels. The author's modernization approach to monitoring the processes of providing social services allows to investigate the real state of the main indicators influencing these processes and to identify problem regions in order to develop mechanisms to stimulate their development. -
Mobile wallets have been in continuous demand and developed over the past few years, especially during the COVID-19 pandemic. Several studies have examined user intentions and perspectives. This study develops a conceptual model combining behavioral factors with the technology acceptance model (TAM). The goal is to identify key factors that influence user's intention to adopt mobile payments. This study uses the TAM and the unified theory of acceptance and use of technology (UTAUT) models with additional factors. The additional factors are security, trust, facilitating conditions, and lifestyle compatibility. The study analyzes the results of a survey of 394 Saudi citizens conducted via an online survey. The results indicate that user attitudes and intentions are positively influenced by all of the factors. Perceived usefulness, perceived ease of use, lifestyle compatibility, and facilitating conditions are direct predictors of user behavior in accepting mobile wallet payments. This study provides an empirical contribution to the literature on mobile payment acceptance on the effect of perceived usefulness and lifestyle compatibility. The results demonstrate that about 26% of the respondents started using mobile wallet services because of the COVID-19 pandemic.
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Prykhodkina, Nataliia;Tymoshko, Hanna;Zuieva, Alona;Sholokh, Olena;Noskova, Margaryta;Lebid, Yuliia 231
The article assesses the functioning of the DE system or a separate educational institution, where it can be carried out on the basis of developed criteria or on a regulatory basis. The assessment was carried out on the basis of a qualitative and quantitative nature by comparing the actual state of affairs with a certain "ideal" (educational standard), which must be defined and used as a kind of benchmark against which the assessment is made. Conducted an assessment based on a regulatory framework that represents an alternative approach. It has been emphasized that the exceptional difficulty in determining the ideal indicators (norms) of the activities of universities, it has been found that the normative approach, in which the activities of traditional and open universities are compared, taking into account the differences in social, cultural and economic conditions, is the most acceptable. -
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy
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Al-Amri, Bayan O;Alsuwat, Hatim;Alsuwat, Emad 245
This work aims to focus on the current features and characteristics of Human Element and Artificial intelligence (AI), ask some questions about future information security, and whether we can avoid human errors by improving machine learning and AI or invest in human knowledge more and work them both together in the best way possible? This work represents several related research results on human behavior towards information security, specified with elements and factors like knowledge and attitude, and how much are they invested for ISA (information security awareness), then presenting some of the latest studies on AI and their contributions to further improvements, making the field more securely advanced, we aim to open a new type of thinking in the cybersecurity field and we wish our suggestions of utilizing each point of strengths in both human attributions in software security and the existence of a well-built AI are going to make better future software security. -
Vasilyev, G.S.;Kuzichkin, O.R.;Surzhik, D.I. 252
The application of the principle of trigeneration allows to simultaneously provide electricity to power electronic devices, as well as heat and cold to create the necessary microclimate of the premises and increase efficiency compared to separate cooling and heating systems. The use of Peltier thermoelectric modules (TEM) as part of trigenerative systems allows for smooth and precise control of the temperature regime, high manufacturability and reliability due to the absence of moving parts, resistance to shock and vibration, and small weight and size parameters of the system. One of the promising areas of improvement of trigenerative systems is their modeling and optimization based on the automatic control theory. A block diagram and functional model of an energy-saving trigenerative climate control system based on Peltier modules are developed, and the transfer functions of an open and closed system are obtained. The simulation of the transient characteristics of the system with varying parameters of the components is performed. The directions for improving the quality of transients in the climate control system are determined, as well as the prospects of the proposed methodology for modeling and analyzing control systems operating in substantially nonlinear modes. -
Online learning systems are becoming an effective educational medium for many universities. The accessibility of online learning system in universities means that every student, including the visually impaired, is able use all the site's services. This research focuses on investigating the accessibility of online learning systems for visually impaired users. The paper purpose is to understand the perception of visually impaired undergraduate students towards Blackboard's accessibility and to make recommendations for a new Blackboard design with accessible features that support their needs. Impact of a new Blackboard design with accessible features on visually impaired students, using Taif University students as a case study is evaluated in this paper, as it is similar to most learning systems used by Saudi universities. A study on Taif University's utilization of Blackboard was conducted using mixed method approaches (an automatic tool and a user study). In the first phase, Taif's use of Blackboard was evaluated by the web accessibility tool called AChecker. In the second phase, we conducted a user study to verify previously discovered accessibility challenges to fully assess them according to the accessibility and usability guidelines. In this study, the accessibility of Taif University's Blackboard was evaluated by thirteen visually impaired undergraduate students. The results of the study show that Blackboard has accessibility issues, which are confusing navigation, incompatibility with assistive technologies, untitled pages or parts, unclear identification for visual elements, and inaccessible PDF files. This paper also introduces a set of recommendations that aim to improve the accessibility of Blackboard and other educational websites developed for this population. It also highlights the serious need for universities to enhance web accessibility for online learning systems for students with disabilities.
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Businesses and manufacturing have benefited from the evolution of digital information technology. The introduction of e-commerce has changed the way companies are conducted, and the manufacturing industry is using emerging technologies to automate and synchronize production processes in order to increase productivity and profitability. The results of the study show that incorporating the internet into e-commerce has transformed the process, making it one of the most advanced and high users of digital technology. E-commerce has advanced by leaps and bounds, allowing products and services to flow electronically with minimal delays. Manufacturing has benefited from the implementation of IoT, which has increased the productivity of production processes and is gradually becoming a major beneficiary of modern computer technology.
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Alkinani, Edrees A.;Alzahrani, Abdullah I.A. 275
Ministries of Education are integrating different Learning Management Systems (LMS) to enhance teaching and learning during the lockdown to avoid academic loss. The key factor for delivering a high-quality education through LMS platforms is teachers' acceptance and adoption of the platform. Madrasati platform (which means My school) was introduced by Saudi Arabian Ministry of education as the formal teaching and learning for distance education for public education levels. This study aims to examine the effectiveness, usability and adoption of "Madrasati" platform from teachers' perspectives in Saudi Arabia. "SUS, CSUQ" tests were used to test the usability of the new platform. Using quantitative research design, data were collected using questionnaire. 200 teachers were selected randomly answered the survey. Data was analysed descriptively and inferentially using SPSS (25). The results obtained indicate that the teachers are highly satisfied using Madrasati platform and technically it is well designed. Also, Madrasati has positive effect on teaching quality. Moreover, Madrasati has high usability in teaching. One of the key findings were that the quality of the information content in Madrasati has a strong effect on teachers' perception of the Madrasati usefulness that led to a positive attitude towards Madrasati. These findings would be useful to the ministry of education and institutions trying to integrate technology in their teaching and learning processes. Thus, this paper contributes towards more effective utilisation of the extensive functionalities that Madrasati have to offer, which will contribute toward the development of pedagogy in Saudi Arabia. -
Abdulrahman, Ammar;Hashem, Khalid;Adnan, Gaze;Ali, Waleed 286
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. -
Shobana, Gopalakrishnan;Arockia, Xavier Annie R. 294
VANET is an upcoming technology with an encouraging prospect as well as great challenges, specifically in its security. This paper intends to survey such probable attacks and the correlating detection mechanisms that are introduced in the literature. Accordingly, administering security and protecting the owner's privacy has become a primary argument in VANETs. To furnish stronger security and preserve privacy, one should recognize the various probable attacks on the network and the essence of their behavior. This paper presents a comprehensive survey on diversified attacks and the recommended unfolding by the various researchers which concentrate on security services and the corresponding countermeasures to make VANET communications more secure. -
The relevance of the research involves outlining the need for modern professionals to acquire new competencies. In the conditions of rapid civilizational progress, in order to meet the requirements of the labor market in the knowledge society, there is a readiness for continuous training as an indicator of professional success. The purpose of the research is to identify the impact of various forms of application of information technologies for lifelong learning in order to provide the continuous self-development of each person without cultural or age restrictions and on the basis of rapid digital progress. A high level (96%) of need of the adult population in continuing education with the use of digital technologies has been established. The most effective ways to implement the concept of "lifelong learning" have been identified (educational camps, lifelong learning, mass open online courses, Makerspace activities, portfolio use, use of emoji, casual game, scientific research with iVR game, implementation of digital games, work in scientific cafes). 2 basic objectives of continuing professional education for adults have been outlined (continuous improvement of qualifications and obtaining new qualifications). The features of ICT application in adult education have been investigated by using the following methods, namely: flexibility in terms of easy access to ideas, solving various problems, orientation approach, functional learning, group or individual learning, integration of leisure, personal and professional activities, gamification. The advantages of application of information technologies for continuous education (economic, time, and adaptive) have been revealed. The concept of continuous adult learning in the context of digitalization has been concluded. The research provides a description of the structural principles of the concept of additional education; a system of information requests of the applicant, as well as basic technologies for lifelong learning. The research indicates the lack of comprehensive research in the relevant field. The practical significance of the research results lies in the possibility of using the obtained results for a wider acquaintance of the adult population with the importance of the application of lifelong learning for professional activities and the introduction of methods for its implementation in the educational policy of the state.
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Alqethami, Sara;Almutanni, Badriah;AlGhamdi, Manal 312
Lack of knowledge and digital skills is a threat to the information security of the state and society, so the formation and development of organizational culture of information security is extremely important to manage this threat. The purpose of the article is to assess the state of information security of the state and society. The research methodology is based on a quantitative statistical analysis of the information security culture according to the EU-27 2019. The theoretical basis of the study is the theory of defense motivation (PMT), which involves predicting the individual negative consequences of certain events and the desire to minimize them, which determines the motive for protection. The results show the passive behavior of EU citizens in ensuring information security, which is confirmed by the low level of participation in trainings for the development of digital skills and mastery of basic or above basic overall digital skills 56% of the EU population with a deviation of 16%. High risks to information security in the context of damage to information assets, including software and databases, have been identified. Passive behavior of the population also involves the use of standard identification procedures when using the Internet (login, password, SMS). At the same time, 69% of EU citizens are aware of methods of tracking Internet activity and access control capabilities (denial of permission to use personal data, access to geographical location, profile or content on social networking sites or shared online storage, site security checks). Phishing and illegal acquisition of personal data are the biggest threats to EU citizens. It have been identified problems related to information security: restrictions on the purchase of products, Internet banking, provision of personal information, communication, etc. The practical value of this research is the possibility of applying the results in the development of programs of education, training and public awareness of security issues. -
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.