Technology Group (38) at IgMin Research

The global move towards Electric Vehicles (EVs) marks a crucial step towards sustainable transportation. However, effectively integrating EVs into the current infrastructure demands more than technological advancements. One of the key challenges is optimizing the routing of EVs to minimize costs and environmental impact. This editorial examines the role of Machine Learning (ML) in addressing the electric vehicle routing problem (ESVRP), highlighting its potential to transform cost optimization and sustainability in transportation. Routing is a ...fundamental part of transportation logistics, influencing efficiency, cost, and environmental impact. While traditional internal combustion engine vehicles have established routing systems, EVs present unique challenges such as limited battery capacity, longer refueling times, and fewer charging stations. These factors require advanced routing solutions that can dynamically adapt to various constraints.Read more

This study explores the imperative to modernize cloud computing in response to evolving technological landscapes and business demands. The research examines key areas requiring urgent attention to maintain competitive edge and operational efficiency in the digital era. We investigate the critical need for data center upgrades to accommodate emerging technologies and increased workloads. The study also delves into enhanced security measures and data privacy compliance protocols necessary to protect sensitive information in an increasingly comple...x threat environment. Furthermore, we analyze strategies to meet the growing demand for performance and scalability, ensuring that cloud infrastructure can adapt to fluctuating business needs. The research emphasizes the importance of maintaining system reliability to minimize downtime and maximize productivity. Additionally, we explore the potential of hybrid cloud integration to enable innovation and reduce costs. The purpose of this study is to provide a comprehensive overview of the challenges and opportunities in modernizing cloud computing, offering insights for organizations seeking to optimize their cloud strategies. Key findings highlight the significance of proactive infrastructure management, the adoption of advanced security frameworks, and the implementation of flexible, scalable architectures. This research contributes to the body of knowledge on cloud computing modernization, serving as a valuable resource for IT professionals, business leaders, and researchers in the field.Read more

In this paper, we present a deep CNN-based approach for multi-class classification of three-dimensional (3-D) objects using phase-only digital holographic information. The 3-D objects considered for the multi-class (four-class) classification task are ‘triangle-square’, ‘circle-square’, ‘square-triangle’, and ‘triangle-circle’. The 3-D object ‘triangle-square’ is considered for Class-1 and the remaining 3-D objects ‘circle-square’, ‘square-circle’, and ‘tr...iangle-circle’ are considered for Class-2, Class-3, and Class-4. The digital holograms of 3-D objects were created using the two-step Phase-Shifting Digital Holography (PSDH) technique and were computationally post-processed to obtain phase-only digital holographic data. Subsequently, a deep CNN was trained on a phase-only image dataset consisting of 2880 images to produce the results. The loss and accuracy curves are presented to validate the performance of the model. Additionally, the results are validated using metrics such as the confusion matrix, classification report, Receiver Operating Characteristic (ROC) curve, and precision-recall curve.Read more

An experimental investigation was carried out to study the effect of circular cross-holes on the failure behavior of unidirectional glass fiber reinforced with unsaturated polyester resin composites, varying cross-ply laminates that were subjected to axial tensile load. This paper deals with the effects of the circular notch and the number of plies on nominal tensile and net tensile strengths. Tensile strengths were investigated for composites with cross-ply([0/90]. [90°/0°/90°] and [0°/90°/0°].90°), orientation and ...varying the laminate layers with a central hole, and effects of volume fraction and number of ply on mechanical properties for un-notched (smooth) and notched specimens were also studied. The results showed that increasing the number of plies has a marginal effect on tensile strength values. The fraction of volume has significant effects and for increasing the number of plies about 9% decreases in nominal tensile strength and about 11% decrease in the net tensile strength was observed. The same results were obtained with finite element analysis.Read more

Pneumonia is described as an acute infection of lung tissue produced by one or more bacteria, and Coronavirus Disease (COVID-19) is a deadly virus that affects the lungs of the human body. The symptoms of COVID-19 disease are closely related to pneumonia. In this work, we identify the patients of pneumonia and coronavirus from chest X-ray images. We used a convolutional neural network for spatial feature learning from X-ray images. We experimented with pneumonia and coronavirus X-ray images in the Kaggle dataset. Pneumonia and corona patients a...re classified using a feed-forward neural network and hybrid models (CNN+SVM, CNN+RF, and CNN+Xgboost). The experimental findings on the Pneumonia dataset demonstrate that CNN detects Pneumonia patients with 99.47% recall. The overall experiments on COVID-19 x-ray images show that CNN detected the COVID-19 and pneumonia with 95.45% accuracy.Read more

Markov decision process is a dynamic programming algorithm that can be used to solve an optimization problem. It was used in applications like robotics, radar tracking, medical treatments, and decision-making. In the existing literature, the researcher only targets a few applications area of MDP. However, this work surveyed the Markov decision process’s application in various regions for solving optimization problems. In a survey, we compared optimization techniques based on MDP. We performed a comparative analysis of past work of other r...esearchers in the last few years based on a few parameters. These parameters are focused on the proposed problem, the proposed methodology for solving an optimization problem, and the results and outcomes of the optimization technique in solving a specific problem. Reinforcement learning is an emerging machine learning domain based on the Markov decision process. In this work, we conclude that the MDP-based approach is most widely used when deciding on the current state in some environments to move to the next state.Read more

The experimental team has developed a technology of step-by-step low-temperature diffusion doping of gadolinium and scandium into silicon that allows the clusters of impurity atoms to be uniformly distributed throughout the entire bulk of the silicon material. It was shown that, unlike the samples obtained under the high-temperature diffusion doping technology, in the samples obtained under the novel technology the team had detected any surface erosion or formation of alloys and silicide in the near-surface region. The authors have revealed inc...reased thermostability and radiation resistance of silicon samples dotted with clusters of impurity atoms of gadolinium and scandium.The authors have conducted comprehensive studies by using the techniques of tagged atoms, autoradiography, measurement of conductivity and Hall effect, isothermal relaxation of capacitance and current of diffusion, solubility, and electrophysical properties of scandium in silicon under various doping conditions and for a wide temperature range (1100 ÷ 1250 0С). Diffusion parameters, solubility and acceptor nature of scandium in silicon as well as thermal stability of silicon doped with gadolinium and scandium impurity atoms have been established.Read more

In materials science, the integrity and completeness of datasets are critical for robust predictive modeling. Unfortunately, material datasets frequently contain missing values due to factors such as measurement errors, data non-availability, or experimental limitations, which can significantly undermine the accuracy of property predictions. To tackle this challenge, we introduce an optimized K-Nearest Neighbors (KNN) imputation method, augmented with Deep Neural Network (DNN) modeling, to enhance the accuracy of predicting material properties.... Our study compares the performance of our Enhanced KNN method against traditional imputation techniques—mean imputation and Multiple Imputation by Chained Equations (MICE). The results indicate that our Enhanced KNN method achieves a superior R² score of 0.973, which represents a significant improvement of 0.227 over Mean imputation, 0.141 over MICE, and 0.044 over KNN imputation. This enhancement not only boosts the data integrity but also preserves the statistical characteristics essential for reliable predictions in materials science.Read more

In single colloidal nanocrystals of narrow-gap semiconductors PbS and InSb, current instability in the form of quasi-periodic spikes and current resonance peaks was studied by measuring on a scanning probe microscope and analyzing Current-Voltage Characteristics (CVC). The observed phenomena are explained in models of the wave de Broglie process and Bloch oscillations. Statistically, the percentages of such samples and the parameters of oscillations on the current-voltage characteristic are higher, the larger the size quantization parameter, de...termined by the de Broglie wavelength. A possible practical use is the generation and recording of terahertz radiation modulated by ultrashort pulses.Read more

This work is carried out to fill the epistemic and hermeneutical gap within Igbo socio-metaphysical space. Notable works have been done in Igbo studies, however, the place of the Dibia in Igbo ontology has been neglected, hence the current work. The researchers combined library and fieldwork approaches in executing this work. In the end, the research notes that modern Igbo people have neglected the place and activities of the Dibia. Noting that the Dibia being the active presence of the God[s] among the people is an indispensable persona in the... Igbo existence. Therefore, the current crisis of relevance the people are experiencing is a result of this neglect. The works, therefore, conclude that if the Dibia and her activities are brought back to the centre stage of Igbo existence, the socio-political and economic crises of the moment would have been a history.Read more

Energy Valley Optimizer (EVO) is one of the recent metaheuristic algorithms. It draws inspiration from advanced principles in physics related to particle stability and decay modes. This paper presents a new Energy Valley Optimizer (EVO) and levy flights that are hybrid to improve the EVO in solving optimization problems. Levy flight is one of the most important randomization techniques. Fifteen mathematical test functions (five unimodal functions, four multimodal functions, and six composite functions) are solved with the proposed algorithm. We... also compare our results with previous results of metaheuristic algorithms. The statistical results show that the results of the Levy Energy Valley Optimizer (LEVO) outperform other algorithms in almost all mathematical test functions.Read more

Cyber Threat Analysis (CTA) in current conflicts focuses on identifying, monitoring, and assessing potential cyber threats. The main objective is to understand how the parties to the conflict use information and network technologies to conduct warfare, espionage, disinformation, or attacks on digital infrastructure. This analysis includes the identification of actors involved in conflicts and their targets in cyberspace, as well as the analysis of techniques and tools used in cyber activities. The aim of the article is to identify the role of c...yber threat analysis in the context of two current conflicts - the war in Ukraine and the Hamas attack on Israel. The article describes the essence of cyber threat analysis. The research problem focuses on identifying and understanding the role of cyber threat analysis in these conflicts and their potential consequences for international security and regional stability. Through the analysis of reports, online sources, and scientific literature, the article aims to familiarize readers with the role of cyber threat analysis in the context of these conflicts and to propose possible strategies for cyber risk management in the face of contemporary armed conflicts. The results of the analysis of cyber threats (CTA) in current conflicts include the identification of specific actors operating in cyberspace, their modus operandi, their objectives, and the techniques and tools used. In addition, the analysis can reveal existing vulnerabilities in digital security and areas that are particularly vulnerable to attacks. To sum up, it is necessary to create recommendations and directions for changes regarding the strengthening of digital security measures, such as software updates, implementation of network security, and increasing staff awareness of cyber threats. In addition, the CTA may suggest the need for better international cooperation to combat cyber threats and the need to develop strategies to prevent and respond to cyber-attacks.Read more

The “System for Detecting Moving Objects Using 3D LiDAR Technology” introduces a groundbreaking method for precisely identifying and tracking dynamic entities within a given environment. By harnessing the capabilities of advanced 3D LiDAR (Light Detection and Ranging) technology, the system constructs intricate three-dimensional maps of the surroundings using laser beams. Through continuous analysis of the evolving data, the system adeptly discerns and monitors the movement of objects within its designated area. What sets this syste...m apart is its innovative integration of a multi-sensor approach, combining LiDAR data with inputs from various other sensor modalities. This fusion of data not only enhances accuracy but also significantly boosts the system’s adaptability, ensuring robust performance even in challenging environmental conditions characterized by low visibility or erratic movement patterns. This pioneering approach fundamentally improves the precision and reliability of moving object detection, thereby offering a valuable solution for a diverse array of applications, including autonomous vehicles, surveillance, and robotics.Read more

This case study investigates the implementation of Virtual Reality (VR) technology in facilities management Competency-Based Training (CBT) within one of Singapore’s leading facilities and township management companies. Traditionally, workplace training in facility management has relied heavily on lecture-based methods, often resulting in insufficient preparation and a high turnover rate among newly onboarded staff. To address this challenge, this study explores the conversion of training materials into VR-based courseware, enabling immer...sive and interactive learning experiences.Two groups of newly onboarded staff underwent either traditional lecture-based or VR training sessions. Results indicate that those trained using VR exhibited a better comprehension of the material and higher levels of motivation compared to their counterparts in the traditional lecture-based group. However, it was noted that VR training carries the risk of cybersickness, with some participants reporting symptoms such as dizziness. To mitigate this issue, insights from existing literature on cybersickness are incorporated to enhance future VR training materials.This paper studies the application of VR in Facilities Management Competency-based training within the discourse of Industrie 5.0, the latest evolution of industrial technology that emphasizes human-machine collaboration and smart technologies such as VR. By leveraging VR technology for training purposes, this initiative aligns with the principles of Industrie 5.0, fostering innovation and efficiency in facility management practices. Overall, this research underscores the potential of VR in revolutionizing training methodologies within the practices of facilities management while addressing associated challenges to ensure optimal learning outcomes.Read more

Many UK inhabitants now adopt careers working in both university and manufacturing sectors. In a typical engineering career, a young student may now start at university, spend time in factories, often in research and development, and then return to university in later life to teach evolving innovation principles of industry. It is clear to such people that universities are expanding rapidly whereas manufacturing has declined and reached an equilibrium of output but with lower job numbers due to robotics. Manufacturing in 2022 was approximately ...8% of British GDP whereas the university sector was only about 2%. This paper analyses the growing connections and co-locations with numerous universities/manufacturing companies, predicting that convergence in both jobs and GDP contribution will occur around 2050.Read more

Today’s cadet leader is tomorrow’s officer, a professional capable of integrating knowledge and solving complex problems in a full-scale war with incomplete information, being a leader and authority for the personnel. Most of the time, cadets study in study groups using traditional technology, which is not enough to form a professional cadet leader. With the advent of computer technology, students’ and cadets’ motivation to learn using traditional teaching methods has paradoxically begun to decline rapidly. The problem o...f the quality of training of military specialists for the needs of communication and cybersecurity units is not reflected in the publications reviewed. However, significant progress has been made in the use of computer technology as the basis of training tools for ground forces units. Similar training complexes are actively used in NATO member states in the military education system in the training of air force pilots (navigators). Therefore, the author chose the improvement of the educational process at the Department of Combat Use of Communication Units in combination with the methodology of professional-business game for the development of a cadet leader in professional training courses (L-1B) of the tactical level of military education as the direction of research. The purpose of the study and the article is to substantiate the conceptual view of the department in the need to build a computerized training complex for training officers of leaders in professional training courses (L-1B) of the tactical level of military education, capable of using communication units based on computer games. Fragments of the game mechanics of stimulating the interest and motivation of cadets to study, for example, electronic shooting range, and learning Morse code, are considered. Game mechanics is a proof of a professional business game in the course of the discipline: Combat use of military communication systems and complexes of the course of professional military education (professional course of tactical level L1B).Read more

Fractals are complex structures that repeat themselves at several scales. Nature exhibits these in many forms like snowflakes, mountains, coastlines, the human brain/lungs/ nervous system, and many more. It appears that these are nature’s organic way of growth. Thus, there is an underlying science that works to grow or create these self-similar patterns. In this work, tin oxide-based fractals have been grown under laboratory conditions and applied to a gas-sensing field. The facile growth methodology successfully grows fractals on a large... scale. The tin oxide fractals have unique basic building units that connect and grow in different directions. These tin oxide fractals have successfully sensed ethanol vapors in the range of 20 ppm to 100 ppm. The best sensing response has also detected ethanol vapors as low as 10 ppm at room temperature with response and recovery times of 18 ± 3 s and 22 ± 5 s, respectively. The best sensing response recorded for such sensors was under 12 s. The characteristic fractal growth is attributed as the defining factor that enhances ethanol sensing at room temperature.Read more

The pervasive Internet of Things (IoT) integration has revolutionized industries such as medicine, environmental care, and urban development. The synergy between IoT devices and 5G cellular networks has further accelerated this transformation, providing ultra-high data rates and ultra-low latency. This connectivity enables various applications, including remote surgery, autonomous driving, virtual reality gaming, and AI-driven smart manufacturing. However, IoT devices’ real-time and high-volume messaging nature exposes them to potential m...alicious attacks. The implementation of encryption in such networks is challenging due to the constraints of IoT devices, including limited memory, storage, and processing bandwidth. In a previous work [1], we proposed an ongoing key construction process, introducing a pivotal pool to enhance network security. The protocol is designed with a probability analysis to ensure the existence of a shared key between any pair of IoT devices, with the predefined probability set by the system designer. However, our earlier model faced vulnerabilities such as the “parking lot attack” and physical attacks on devices, as highlighted in the conclusion section. We present a complementary solution to address these issues, fortifying our previous protocol against cyber threats. Our approach involves the implementation of an internal Certification Authority (CA) that issues certificates for each IoT device before joining the network.Furthermore, all encryption keys are distributed by the primary IoT device using the Unix OS ‘passwd’ mechanism. If a device “disappears,” all encryption keys are promptly replaced, ensuring continuous resilience against potential security breaches. This enhanced protocol establishes a robust security framework for IoT networks, safeguarding against internal and external threats.Read more

The current study examines the impact of integrating artificial intelligence (AI) in higher education in Saudi Arabia, specifically focusing on the roles of university teachers and students’ learning outcomes. It investigates the transformation of teaching practices, and the challenges faced by university teachers during AI integration, and identifies opportunities for collaboration and innovation. A quantitative research method using a survey questionnaire was employed to gather data and inform practical recommendations. The findings ind...icate that university teachers recognize the potential benefits of AI in education, but also highlight the need to address challenges for effective implementation. Participants perceive AI as a tool that enables more creative and engaging teaching methods, automates tasks, and brings about changes in teaching approaches. Difficulties in utilizing AI effectively and adapting to new teaching methods were identified. AI also offers opportunities for innovation, collaboration, and creating engaging learning environments. There were slight variations in perspectives based on gender and current role, with female university teachers and teaching assistants/professors perceiving higher impact and opportunities for collaboration and innovation. Participants with higher AI expertise perceived a greater impact of AI and fewer challenges. Those who aligned with the educational purposes of AI saw greater impact and opportunities for collaboration while acknowledging usage difficulties.Read more

Sarcasm, a standard social media message, delivers the opposite meaning through irony or teasing. Unfortunately, identifying sarcasm in written text is difficult in natural language processing. The work aims to create an effective sarcasm detection model for social media text data, with possible applications in sentiment analysis, social media analytics, and online reputation management. A hybrid Deep learning strategy is used to construct an effective sarcasm detection model for written content on social media networks. The design emphasizes f...eature extraction, selection, and neural network application. Limited research exists on detecting sarcasm in human speech compared to emotion recognition. The study recommends using Word2Vec or TF-IDF for feature extraction to address memory and temporal constraints. Use feature selection techniques like PCA or LDA to enhance model performance by selecting relevant features. A Capsule Neural Network (CNN) and Long Short-Term Memory (LSTM) collect contextual information and sequential dependencies in textual material. We evaluate Reddit datasets with labelled sarcasm data using metrics like Accuracy. Our hybrid method gets 95.60% accuracy on Reddit.Read more

With the development of science and technology, almost everyone uses 3C (Computer, Communication, and Consumer Electronics) products and can search for information online, play games, chat with others, etc. at any time. Coupled with the impact of the pandemic, school teachers and students cannot go to school and can only use online teaching and distance learning. This requires teachers to quickly learn the relevant knowledge of using information technology for teaching, such as the operation of equipment, online teaching, etc. Establishment of ...meeting rooms. To this day, the Ministry of Education actively promotes digital teaching in primary and secondary schools based on international trends and domestic policy developments. The paper introduced the policy and current state of the application of educational technology in Taiwan.Read more

This study provides a novel way to detect duplicate questions in the Stack Overflow community, posing a daunting problem in natural language processing. Our proposed method leverages the power of deep learning by seamlessly merging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture both local nuances and long-term relationships inherent in textual input. Word embeddings, notably Google’s Word2Vec and GloVe, raise the bar for text representation to new heights. Extensive studies on the Stack Overflow ...dataset demonstrate the usefulness of our approach, generating excellent results. The combination of CNN and LSTM models improves performance while streamlining preprocessing, establishing our technology as a viable piece in the arsenal for duplicate question detection. Aside from Stack Overflow, our technique has promise for various question-and-answer platforms, providing a robust solution for finding similar questions and paving the path for advances in natural language processing.Read more

By measurements on single nanocrystals of indium antimonide in the interelectrode nanogap of a scanning probe microscope, current-voltage characteristics with quasiperiodic current pulsations, are explained in the model of Bloch oscillations in a perfect nanocrystal, and individual sharp peaks - conductivity resonances, explained in the model of quantum-size limitation of the wave process of electron transport in a deep potential hole. The mutual influence of radiation from two statistical ensembles of nanocrystals from the same batch was exper...imentally studied and established. It is assumed that this radiation is entangled photons. It is proposed to use nanocrystals in nanoelectronics as a single-electron memristor, a single-photon bolometer, and a source of microwave radiation.Read more

The aim of the study is to develop a methodology for a professional-business game for the development of a cadet leader in professional training courses (L-1B) of the tactical level of military education on the example of teaching the discipline “Combat use of military communication systems and complexes”. After all, the most important prerequisite for the combat potential of the Armed Forces of Ukraine is a comprehensively trained officer-leader, his consciousness, knowledge, skills, military-technical qualifications, creative and ...innovative activity, developed emotional and volitional sphere of the psyche, beliefs, motivation, and ideals. The article summarizes the research on the state of development of a cadet leader in Ukraine. In the context of a full-scale war, there is a need to strengthen the development of the personality of a cadet leader (a future officer of the tactical level of military education). Today’s cadet-leader is tomorrow’s officer-leader, capable of integrating knowledge and solving complex problems in new or unfamiliar environments with incomplete or limited information, being a leader and authority for the personnel. Therefore, the scientific and practical task arises to consider aspects of the development of the personality of a cadet leader and to modernize the process of his development, for example, in the courses of professional military education L-1B (tactical level of military education) in terms of the disciplines of the Department of Combat Use of Communication Units. The periodization of cadets’ adaptation to the new educational space and service activities is considered, the optimal period of leader growth is determined, and the mechanism itself is proposed. The article separately identifies the key tasks for the selection of academic staff and admission to the educational process. The methodology of the professional and business game for the development of a cadet leader in professional training courses (L-1B) of the tactical level of military education is developed on the example of teaching the discipline “Combat use of military communication systems and complexes”. The scientific novelty of the work is the improved educational process of training and development of a cadet leader in professional training courses (L-1B) of the tactical level of military education by reformatting group classes in the traditional format into a professional business game for the positions of officers with the introduction of typical and atypical (force majeure) training tasks for cadets in everyday activities in new or unfamiliar environments in the presence of incomplete or limited information.Read more

The contemporary landscape of higher education resonates with the dynamic interplay of globalization’s monumental forces, cultural diversity, and transformative educational paradigms. This scholarly inquiry navigates the complex terrain of higher education institutions (HEIs) within the context of the United Arab Emirates (UAE), unraveling the intricate tapestry of educational challenges amidst globalization’s pervasive influence. The research embarks on an odyssey through a holistic examination of HEIs’ responses to global fo...rces, aiming to decipher the transformative journey and its implications for societal transformation. Globalization, a multifaceted phenomenon, has revolutionized higher education, driving a shift from traditional to diversified pedagogical models. However, this transformative journey presents a myriad of challenges and opportunities for HEIs. The study underscores the pivotal role of internationalization and cultural inclusivity in reshaping the educational landscape. Challenges faced by HEIs in adapting to globalized learning environments, reluctance to embrace cultural diversity, and limitations in fostering cross-cultural competencies among students emerge as focal points. This research contributes a novel perspective by delineating the barriers hindering the internationalization of educational settings, offering insights into strategies for fostering inclusive learning environments. Emphasizing the significance of embracing cultural diversity, the study recommends recalibrating institutional policies and practices. These findings resonate beyond academic discourse, influencing practical implications for real-world educational settings. In conclusion, this research emphasizes the imperative for HEIs to adapt and innovate in response to globalization, fostering inclusive learning environments to equip students with the requisite skills for navigating a diverse, interconnected world. Leveraging these insights, institutions can contribute to societal transformation and the holistic development of students, shaping a future aligned with global competencies and enriched cultural understanding.Read more

Semantic segmentation is the most significant deep learning technology. At present, automatic assisted driving (Autopilot) is widely used in real-time driving, but if there is a deviation in object detection in real vehicles, it can easily lead to misjudgment. Turning and even crashing can be quite dangerous. This paper seeks to propose a model for this problem to increase the accuracy of discrimination and improve security. It proposes a Convolutional Neural Network (CNN)+ Holistically-Nested Edge Detection (HED) combined with Spatial Pyr...amid Pooling (SPP). Traditionally, CNN is used to detect the shape of objects, and the edge may be ignored. Therefore, adding HED increases the robustness of the edge, and finally adds SPP to obtain modules of different sizes, and strengthen the detection of undetected objects. The research results are trained in the CityScapes street view data set. The accuracy of Class mIoU for small objects reaches 77.51%, and Category mIoU for large objects reaches 89.95%.Read more

The rapid development of wearable technology provides new opportunities for action data processing and classification techniques. Wearable sensors can monitor the physiological and motion signals of the human body in real-time, providing rich data sources for health monitoring, sports analysis, and human-computer interaction. This paper provides a comprehensive review of motion data processing and classification techniques based on wearable sensors, mainly including feature extraction techniques, classification techniques, and future developmen...t and challenges. First, this paper introduces the research background of wearable sensors, emphasizing their important applications in health monitoring, sports analysis, and human-computer interaction. Then, it elaborates on the work content of action data processing and classification techniques, including feature extraction, model construction, and activity recognition. In feature extraction techniques, this paper focuses on the content of shallow feature extraction and deep feature extraction; in classification techniques, it mainly studies traditional machine learning models and deep learning models. Finally, this paper points out the current challenges and prospects for future research directions. Through in-depth discussions of feature extraction techniques and classification techniques for sensor time series data in wearable technology, this paper helps promote the application and development of wearable technology in health monitoring, sports analysis, and human-computer interaction.Index Terms: Activity recognition, Wearable sensor, Feature extraction, ClassificationRead more

Cinema, apart from its entertaining prospect, can flourish knowledge in various fields of daily life and, in particular, in the understanding of the natural world. It could be even embedded in the educational system, at all levels, so as to convey concepts that may be difficult for students in an enjoyable and understandable way. In this mini-review, the educational dimension of the cinema and the related television series is discussed, through the presentation of concepts and phenomena relevant to the natural sciences that have been approached... with characteristic scenes of popular cinema movies of our times. We present case studies from physics and astronomy, mathematics, materials science, and engineering that can be easily embedded in the educational curriculum. Notwithstanding the fact that watching movies cannot replace the traditional educational method, it can pose as an alternative, complementary tool for educators.Read more

The currently existing Russian and other legislation, as well as special literature, do not contain a methodology for the formation of the business reputation of the organization’s personnel and as a result, there is no unambiguous perception of the business, professional qualities of personnel as an object of evaluation of the organization in the market system. Therefore, it is impossible to single out the share of the results of intellectual advantage in the goods and services produced. This confirms the requirements formulated in para...graph 4 of Accounting Regulation 14/2007 «Accounting for intangible assets», which states that the intellectual and business qualities of the organization’s personnel, their qualifications, and their ability to work are not included in the intangible assets (since they are inseparable from their carriers and cannot be used without them). Meanwhile, the interests of the leading economically developed countries of the world lie in the field of accelerated growth of knowledge acquisition and application of professional skills. This trend of development of economically developed countries forms the basis of competitiveness and efficiency of their work. It should be noted that in the world economy, there is insufficient theoretical elaboration and a special practical significance and relevance of the issue of assessing the business reputation of the personnel of an economic entity. The article presents a comprehensive methodology for evaluating the performance of industrial and production personnel (the standard-production methodology), which contributes to the formation of accounting and information support for the analysis of the activities of both structural divisions, responsibility centers, business segments and commercial organizations as a whole. The author’s standard-production methodology makes it possible to determine the business reputation of industrial and production personnel, which contradicts the official economic paradigm.Read more

People are suffering from” data obesity” as a result of the expansion and quick development of various Artificial Intelligence (AI) technologies and machine learning fields. The management of the current techniques is becoming more challenging due to the data created in the Smart-Health and Fintech service sectors. To provide stable and reliable methods for processing the data, several Machine Learning (ML) techniques were applied. Due to privacy-related issues with the aforementioned two providers, ML cannot fully use the data, whi...ch becomes difficult since it might not give the results that were expected. When the misuse and exploitation of personal data were gaining attention on a global scale and traditional machine learning (CML) was facing difficulties, Google introduced the concept of Federated Learning (FL). In order to enable the cooperative training of machine learning models among several organizations under privacy requirements, federated learning has been a popular research area. The expectation and potential of federated learning in terms of smart-health and fintech services are the main topics of this research.Read more