Ensuring the security of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) is paramount to safeguarding the reliability and safety of critical infrastructure. This paper addresses the significant threat posed by reconnaissance attacks on SCADA/ICS networks and presents an innovative methodology for enhancing their protection. The proposed approach strategically employs imbalance dataset handling techniques, ensemble methods, and feature engineering to enhance the resilience of SCADA/ICS systems. Experimentation and analysis demonstrate the compelling efficacy of our strategy, as evidenced by excellent model performance characterized by good precision, recall, and a commendably low false negative (FN). The practical utility of our approach is underscored through the evaluation of real-world SCADA/ICS datasets, showcasing superior performance compared to existing methods in a comparative analysis. Moreover, the integration of feature augmentation is revealed to significantly enhance detection capabilities. This research contributes to advancing the security posture of SCADA/ICS environments, addressing a critical imperative in the face of evolving cyber threats.

Recently, the development of wireless network technologies has been advancing in several directions: increasing data transmission speed, enhancing user mobility, expanding the range of services offered, improving the utilization of the radio frequency spectrum, and enhancing the intelligence of network and subscriber equipment. In this research, a series of contradictions has emerged in the field of wireless network technologies, with the most acute being the contradiction between the growing demand for wireless communication services (on operational frequencies) and natural limitations of frequency resources, in addition to the contradiction between the expansions of the spectrum of services offered by wireless networks, increased quality requirements, and the use of traditional (outdated) management technologies. One effective method for resolving these contradictions is the application of artificial intelligence elements in wireless telecommunication systems. Thus, the development of technologies for building intelligent (cognitive) radio and cognitive wireless networks is a technological imperative of our time. The functions of artificial intelligence in prospective wireless systems and networks can be implemented in various ways. One of the modern approaches to implementing artificial intelligence functions in cognitive wireless network systems is the application of fuzzy logic and fuzzy processors. In this regard, the work focused on exploring the application of fuzzy logic in prospective cognitive wireless systems is considered relevant.

Nowadays usage of different applications of identity management IDM demands prime attention to clarify which is more efficient regarding preserve privacy as well as security to perform different operations concerning digital identity. Those operations represent the available interactions with identity during its lifecycle in the digital world e.g., create, update, delete, verify and so on. With the rapid growth in technology, this field has been evolving with a number of IDM models being proposed to ensure that identity lifecycle and face some significant issues. However, the control and ownership of data remines in the hand of identity service providers for central and federated approaches unlike in the self-sovereign identity management SSIM approach. SSIM is the recent IDM model were introduced to solve the issue regarding ownership of identity and storing the associated data of it. Thus, SSIM aims to grant the individual's ability to govern their identities without intervening administrative authorities or approval of any authority. Recently, we noticed that numerous IDM solutions enable individuals to own and control their identities in order to adapt with SSIM model. Therefore, we intend to make comparative study as much of these solutions that have proper technical documentation, reports, or whitepapers as well as provide an overview of IDM models. We will point out the existing research gaps and how this study will bridge it. Finally, the study will propose a technical enhancement, everKEY solution, to address some significant drawbacks in current SSIM solutions.

Cloud computing is an emerging business model popularized during the last few years by the IT industry. Providing "Everything as a Service" has shifted many organizations to choose cloud-based services. However, some companies still fear shifting their data to the cloud due to issues related to the security and privacy. The paper suggests a novel Trust based Mutual Authentication Mechanism using Secret P-box based Mutual Authentication Mechanism (TbMAM-SPb) on the criticality of information. It uses a particular passcodes from one of the secret P-box to act as challenge to one party. The response is another passcode from other P-box. The mechanism is designed in a way that the response given by a party to a challenge is itself a new challenge for the other party. Access to data is provided after ensuring certain number of correct challenge-responses. The complexity can be dynamically updated on basis of criticality of the information and trust factor between the two parties. The communication is encrypted and time-stamped to avoid interceptions and reuse. Overall, it is good authentication mechanism without the use of expensive devices and participation of a trusted third party.

As SDN devices and systems hit the market, security in SDN must be raised on the agenda. SDN has become an interesting area in both academics and industry. SDN promises many benefits which attract many IT managers and Leading IT companies which motivates them to switch to SDN. Over the last three decades, network attacks becoming more sophisticated and complex to detect. The goal is to study how traffic information can be extracted from an SDN controller and open virtual switches (OVS) using SDN mechanisms. The testbed environment is created using the RYU controller and Mininet. The extracted information is further used to detect these attacks efficiently using a machine learning approach. To use the Machine learning approach, a dataset is required. Currently, a public SDN based dataset is not available. In this paper, SDN based dataset is created which include legitimate and non-legitimate traffic. Classification is divided into two categories: binary and multiclass classification. Traffic has been classified with or without dimension reduction techniques like PCA and LDA. Our approach provides 98.58% of accuracy using a random forest algorithm.

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

The attack technique by the malware distribution form is a dangerous, difficult to detect and prevent attack method. Current malware detection studies and proposals are often based on two main methods: using sign sets and analyzing abnormal behaviors using machine learning or deep learning techniques. This paper will propose a method to detect malware on Endpoints based on Event IDs using deep learning. Event IDs are behaviors of malware tracked and collected on Endpoints' operating system kernel. The malware detection proposal based on Event IDs is a new research approach that has not been studied and proposed much. To achieve this purpose, this paper proposes to combine different data mining methods and deep learning algorithms. The data mining process is presented in detail in section 2 of the paper.

Makkah Al-Mukarramah is the capital of Islamic world. It receives special attention from the Saudi government's rulers to transform it into a smart city for the benefit of millions of pilgrims. One of the 2030 vision objectives is to transform specific cities to smart ones with advanced technological facilitation, Makkah is one of these cities. The history of Makkah is not well known for some Muslims. As a result, we built the concepts of our application "Ain Makkah" to enable visitors of Makkah to know the history of Makkah by using technology. In particular "Ain Makkah" uses Augmented Reality to view the history of Al-Kaaba. A 3D model will overlay Al-Kaaba to show it in the last years. Our project will use Augmented Reality to build a 3D model to overlay Al-Kaaba. Future work will expand the number of historical landmarks of Makkah.

The main purpose of the study is to analyze the features of the influence of Agile management on the competence of the personnel of the socio-economic system in the digital economy. The research methodology implies the use of modern methods of analysis. Improving the business processes of an organization is associated with improving activities, the formation of effective management systems and processes, especially the organization's policy in the field of quality, rational use of resources, increasing the responsibility of management, social responsibility of the organization, etc. The modern knowledge economy places high demands on the effectiveness of behavioral models of employees of the organization. The role of the human factor in the production system is becoming more and more obvious. Therefore, it is important to study the competence of the personnel of any socio-economic system. Based on the results of the study, the key features of the influence of Agile management on the competence of the personnel of the socio-economic system in the digital economy were identified.

Professional self-determination of the individual is a complex and lengthy process of finding and realizing yourself in the profession. The main goal of professional self-determination is clarified. The basic concepts of readiness for professional self-determination of future specialists in the modern information society are revealed. The following approaches to the consideration of the concept of readiness are defined: functional-psychological, personal, activity-based. Based on the components of readiness identified by the researchers, it can be assumed that the structure of professional self-determination of the future specialist contains motivational, cognitive and activity components. Self-determination is defined as a multidimensional process that can be considered from different points of view: as a series of tasks, that society sets for the emerging individual, and which the individual must solve in a certain period. As a process of step-by-step decision-making, with the help of which the individual forms a balance between his desires and inclinations, on the one hand, and the needs of society, on the other; as a process of forming an individual lifestyle, part of which is professional activity. A number of tasks of professional self-determination of a future specialist in the information society are formulated. Diagnostic practices for determining the degree of readiness of future specialists for future professional success are characterized. Practices are developed as a basis for creating an individually oriented correctional and development program to promote the formation of future specialists' focus on future professional success. Their task is to ensure control over the dynamics of this process, assess the effectiveness of this career guidance work. Practices are aimed at identifying the degree of thorough knowledge of the conditions for achieving professional success in the chosen field of activity among future specialists.

The relevance of the research topic lies in the necessity to use social networks as innovative tools of marketing communications. A wide audience and the ability to segment the market for a specific consumer determine the construction of a corporate strategy, which will be based on using the social networking approach. The spread of the global coronavirus pandemic has led to the rapid development of remote communication channels between the company and the customer. The issue of using marketing tools in social networks acquires the most urgent importance in the modern world of the introduction and implementation of the company's marketing strategies. The purpose of the academic paper is to study the use of social networks as features of implementing the marketing campaign. Social networks are the result of the development of digital technologies and the processes of creating an information society involved in the digital space. The objectives of the research are to analyse the opportunity of using social networks as a tool for marketing communications and their implementation at the level of its widespread use by enterprises and establishments. It is significant to create an advertising campaign by defining the target audience and outlining the key aspects, on which the company is focused. The research methodology consists in determining the theoretical and methodological approaches to the essence of introducing social networks and their practical importance in the implementation of marketing activities of companies. The obtained results can significantly improve the quality of functioning of modern enterprises and organizations that plan to master a new market segment or gain competitive advantages in the existing one. The academic paper examines the essence of social networks as a tool of marketing communications. The key principles of the development of digital social platforms were revealed. The quality of implementing the advertising campaign in the social network was studied, and further prospects for the development of using social networks as a component of the marketing strategy were outlined. Therefore, the academic paper analyses the problems of using social networks as a marketing tool.

The main purpose of the study is to identify the key aspects of modern innovative research in the field of education. In the modern informatized world, education is becoming a decisive factor in social development and an important component in the development of the human personality, increasing respect for human rights and freedoms. Today it is quite obvious that without the necessary education a person will not be able to provide himself with proper living conditions and realize himself as a person. The high level of education of the population is an important factor that positively influences the creation of favorable conditions for the full realization of the rights and freedoms of man and citizen. Today, active and interactive teaching methods are widely used. The use of interactive teaching methods ensures complete immersion of students in the learning process and is the main source of learning. The radical difference between traditional and interactive learning is that the student not only replenishes and strengthens his knowledge, but also complements and constructs new ones. The methodology includes a number of theoretical methods. As a result of the study, current trends and prerequisites of modern innovative research in the field of education were investigated.

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.

The main purpose of the study is to analyze the features of the influence of Agile management on the competence of the personnel of the socio-economic system in the digital economy. The research methodology implies the use of modern methods of analysis. Improving the business processes of an organization is associated with improving activities, the formation of effective management systems and processes, especially the organization's policy in the field of quality, rational use of resources, increasing the responsibility of management, social responsibility of the organization, etc. The modern knowledge economy places high demands on the effectiveness of behavioral models of employees of the organization. The role of the human factor in the production system is becoming more and more obvious. Therefore, it is important to study the competence of the personnel of any socio-economic system. Based on the results of the study, the key features of the influence of Agile management on the competence of the personnel of the socio-economic system in the digital economy were identified.

Electronic educational environments in the conditions of quarantine restrictions of COVID-19 have become a common phenomenon for the organization of distance educational activities. Under the conditions of Russian aggression, Ukrainian proof of their use is unique. The purpose of the article is to analyze the role of electronic educational environments in the process of training applicants for higher education in Ukraine in the realities of a large-scale war. General scientific methods (analysis, synthesis, deduction, and induction) and special pedagogical prognostic methods, modeling, and SWOT analysis methods were used. In the results, the general properties of the Internet educational platforms common in Ukraine, the peculiarities of using the Moodle and Prometheus platforms, and an approximate model of the electronic learning environment were discussed. The reasons for the popularity of Moodle among Ukrainian universities are analyzed, but vulnerable elements related to security are emphasized. It was also determined that the high cost of Prometheus software and less functionality made this learning environment less relevant. The conclusions state that the military actions drew the attention of universities in Ukraine to the formation of their own educational platforms. This is especially relevant for technical and military institutions of higher education.

The main purpose of the study is to identify the key aspects of the formation of legal and professional competence of students of higher educational institutions in the context of the COVID-19 pandemic. The modern system of public relations tightens the requirements for the professional and legal competence of specialists in all spheres of life. The development of a unified nationwide strategy in the field of education focused on the formation and development of young people's skills for life in the information society, is aimed at finding ways to form an active position of a future specialist, developing an experience of a holistic understanding of the professional activity, systemic action in solving new problems and tasks. The methodology includes a number of theoretical methods. Based on the results of the study, the main elements of the formation of legal and professional competence of students of higher educational institutions in the context of the COVID-19 pandemic.

The main purpose of the study is to analyze the features of state regulation of the labor market in a crisis. Structural shifts in the labor market are due to the transformation of public and economic relations in today's globalized world. Increasing competition, the development of the knowledge economy, information technology, changes in the content and forms of labor require updating the labor market regulation system. The research methodology implies the use of modern methods of analysis. The analysis of the features of state regulation of the labor market in crisis conditions is carried out.

Large software systems are being produced with a noticeably higher level of quality with component-based software engineering (CBSE), which places a strong emphasis on breaking down engineered systems into logical or functional components with clearly defined interfaces for inter-component communication. The component-based software engineering is applicable for the commercial products of open-source software. Software metrics play a major role in application development which improves the quantitative measurement of analyzing, scheduling, and reiterating the software module. This methodology will provide an improved result in the process, of better quality and higher usage of software development. The major concern is about the software complexity which is focused on the development and deployment of software. Software metrics will provide an accurate result of software quality, risk, reliability, functionality, and reusability of the component. The proposed metrics are used to assess many aspects of the process, including efficiency, reusability, product interaction, and process complexity. The details description of the various software quality metrics that may be found in the literature on software engineering. In this study, it is explored the advantages and disadvantages of the various software metrics. The topic of component-based software engineering is discussed in this paper along with metrics for software quality, object-oriented metrics, and improved performance.

The current electricity networks will undergo profound changes in the years to come to be able to meet the growing demand for electricity, while minimizing the costs of consumers and producers, etc. The electricity network of tomorrow or even the intelligent « Smart Grids » network will be the convergence of two networks: the electricity network and the telecommunications network. In this context falls our work which aims to study the impact of the integration of energy decentralization into the electricity network. In this sense, we have implemented a new smart grid model where several coexisting suppliers can exchange information with consumers in real time. In addition, a new approach to energy distribution optimization has been developed. The simulation results prove the effectiveness of this approach in improving energy exchange and minimizing consumer purchase costs and line losses.

In recent decades, the growth of communication technology has resulted in an explosion of data-related information. Ontology perception is being used as a growing requirement to integrate data and unique functionalities. Ontologies are not only critical for transforming the traditional web into the semantic web but also for the development of intelligent applications that use semantic enrichment and machine learning to transform data into smart data. To address these unclear facts, several researchers have been focused on expanding ontologies and semantic web technologies. Due to the lack of clear-cut limitations, ontologies would not suffice to deliver uncertain information among domain ideas, conceptual formalism supplied by traditional. To deal with this ambiguity, it is suggested that fuzzy ontologies should be used. It employs Ontology to introduce fuzzy logical policies for ambiguous area concepts such as darkness, heat, thickness, creaminess, and so on in a device-readable and compatible format. This survey efforts to provide a brief and conveniently understandable study of the research directions taken in the domain of ontology to deal with fuzzy information; reconcile various definitions observed in scientific literature, and identify some of the domain's future research-challenging scenarios. This work is hoping that this evaluation can be treasured by fuzzy ontology scholars. This paper concludes by the way of reviewing present research and stating research gaps for buddy researchers.

Diabetes is a condition that can be brought on by a variety of different factors, some of which include, but are not limited to, the following: age, a lack of physical activity, a sedentary lifestyle, a family history of diabetes, high blood pressure, depression and stress, inappropriate eating habits, and so on. Diabetes is a disorder that can be brought on by a number of different factors. A chronic disorder that may lead to a wide range of complications. Diabetes mellitus is synonymous with diabetes. There is a correlation between diabetes and an increased chance of having a variety of various ailments, some of which include, but are not limited to, cardiovascular disease, nerve damage, and eye difficulties. There are a number of illnesses that are connected to kidney dysfunction, including stroke. According to the figures provided by the International Diabetes Federation, there are more than 382 million people all over the world who are afflicted with diabetes. This number will have risen during the years in order to reach 592 million by the year 2035. There are a substantial number of people who become victims on a regular basis, and a significant percentage of those people are uninformed of whether or not they have it. The individuals who are most adversely impacted by it are those who are between the ages of 25 and 74 years old. This paper reviews about various machine learning techniques used to detect diabetes mellitus.

In recent years, popularity of deep learning (DL) is increased due to its ability to extract features from Hyperspectral images. A lack of discrimination power in the features produced by traditional machine learning algorithms has resulted in poor classification results. It's also a study topic to find out how to get excellent classification results with limited samples without getting overfitting issues in hyperspectral images (HSIs). These issues can be addressed by utilising a new learning network structure developed in this study.EfficientNet-B4-Based Convolutional network (EN-B4), which is why it is critical to maintain a constant ratio between the dimensions of network resolution, width, and depth in order to achieve a balance. The weight of the proposed model is optimized by Search and Rescue Operations (SRO), which is inspired by the explorations carried out by humans during search and rescue processes. Tests were conducted on two datasets to verify the efficacy of EN-B4, with Indian Pines (IP) and the University of Pavia (UP) dataset. Experiments show that EN-B4 outperforms other state-of-the-art approaches in terms of classification accuracy.

The main purpose of the study is to analyze the linguoculturological foundations of in-depth study of the national language. The new requirements facing a modern teacher, his training and professional qualities make it necessary to take into account the experience and latest achievements of other countries in the field of educational policy, in particular, in the field of teaching foreign languages, as well as to identify and overcome negative ones. Based on the results of the study, the key linguoculturological foundations of in-depth study of the national language were characterized.

In this era of information and communication technology (ICT), tremendous improvements have been witnessed in our daily lives. The impact of these technologies is subjective and negative or positive. For instance, ICT has brought a lot of ease and versatility in our lifestyles, on the other hand, its excessive use brings around issues related to physical and mental health etc. In this study, we are bridging these both aspects by proposing the idea of AI based mental healthcare (AIMS). In this regard, we aim to provide a platform where the patient can register to the system and take consultancy by providing their assessment by means of a chatbot. The chatbot will send the gathered information to the machine learning block. The machine learning model is already trained and predicts whether the patient needs a treatment by classifying him/her based on the assessment. This information is provided to the mental health practitioner (doctor, psychologist, psychiatrist, or therapist) as clinical decision support. Eventually, the practitioner will provide his/her suggestions to the patient via the proposed system. Additionally, the proposed system prioritizes care, support, privacy, and patient autonomy, all while using a friendly chatbot interface. By using technology like natural language processing and machine learning, the system can predict a patient's condition and recommend the right professional for further help, including in-person appointments if necessary. This not only raises awareness about mental health but also makes it easier for patients to start therapy.

Smart applications based on the Network of Everything also known as Internet of Everything (IoE) are increasing popularity as network connectivity requires rise further. As a result, there will be a greater need for developing 6G technologies for wireless communications in order to overcome the primary limitations of visible 5G networks. Furthermore, implementing neural networks into 6G will bring remedies for the most complex optimizing networks challenges. Future 6G mobile phone networks must handle huge applications that require data and an increasing amount of users. With a ten-year time skyline from thought to the real world, it is presently time for pondering what 6th era (6G) remote correspondence will be just before 5G application. In this article, we talk about 6G dreams to clear the street for the headway of 6G and then some. We start with the conversation of imaginative 5G organizations and afterward underline the need of exploring 6G. Treating proceeding and impending remote organization improvement in a serious way, we expect 6G to contain three critical components: cell phones super broadband, very The Web of Things (or IoT and falsely clever (artificial intelligence). The 6G project is currently in its early phases, and people everywhere must envision and come up with its conceptualization, realization, implementation, and use cases. To that aim, this article presents an environment for Presented Distributed Artificial Intelligence as-a-Services (DAIaaS) supplying in IoE and 6G applications. The case histories and the DAIaaS architecture have been evaluated in terms of from end to end latency and bandwidth consumption, use of energy, and cost savings, with suggestion to improve efficiency.