It has been demonstrated that technologies and methods of intelligent data analysis (IDA) in the educational domain, particularly based on the analysis of digital traces (DT) of students, offer substantial opportunities for analyzing student activities. Notably, the DT of students are generated both during remote learning sessions and during blended learning modes. By applying IDA methods to DT, one can obtain information that is beneficial for both the educator in a specific discipline and for the educational institution's management. Such information might pertain to various aspects of the functioning of the digital educational environment (DEE) of the institution, such as: the student's learning style; individual preferences; the amount of time dedicated to a specific task, among others. An algorithm has been proposed for constructing a process model in the DEE based on log analysis within the DEE. This algorithm facilitates the description of a specific process in the DEE as a hierarchy of foundational process elements. Additionally, a model based on cluster analysis methods has been proposed, which may prove beneficial for analyzing the registration logs of systemic processes within the university's DEE. Such an analysis can potentially aid in detecting anomalous behavior of students and other individuals within the university's DEE. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE.
This paper stipulates several technological research and development thrusts that can assist in modern day approaches to simulated training of minimally invasive laparoscopic and robot surgery. Basic tenets of such training are explained, and specific areas of research are enumerated. Specifically, augmented and mixed reality are proposed as a means of improving perceptual and clinical decision-making skills, haptics are proposed as mechanism not only to provide force feedback and guidance, but also as a means of reflecting a tactile feel of surgery in simulated training scenarios. Learning optimization is discussed to fine tune the difficulty levels of various exercises. All the above elements can serve as the foundation for building computer-based virtual coaching environments that can reduce the training costs and provide a broader access to learning highly complex, technology driven surgical techniques.
In the article there are presented results of the study of the state of user competencies for different specialties of the university digital educational environment (UDEE) on issues related to information security (IS). The methods of cluster analysis and analysis of digital (electronic) traces (DT) of users are used. On the basis of analyzing the DTs of different groups of registered users in the UDEE, 6 types of users are identified. These types of users were a result of applying hierarchical classification and k-means method. Users were divided into appropriate clusters according to the criteria affecting IS risks. For each cluster, the UDEE IS expert can determine the probability of occurrence of high IS risk incidents and, accordingly, measures can be taken to address the causes of such incidents. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE.
The potential breach of access to confidential content hosted in a university's Private Academic Cloud (PAC) underscores the need for developing new protection methods. This paper introduces a Threat Analyzer Software (TAS) and a predictive algorithm rooted in both an operational model and discrete threat recognition procedures (DTRPs). These tools aid in identifying the functional layers that attackers could exploit to embed malware in guest operating systems (OS) and the PAC hypervisor. The solutions proposed herein play a crucial role in ensuring countermeasures against malware introduction into the PAC. Various hypervisor components are viewed as potential threat sources to the PAC's information security (IS). Such threats may manifest through the distribution of malware or the initiation of processes that compromise the PAC's security. The demonstrated counter-threat method, which is founded on the operational model and discrete threat recognition procedures, facilitates the use of mechanisms within the HIPV to quickly identify cyber attacks on the PAC, especially those employing "rootkit" technologies. This prompt identification empowers defenders to take swift and appropriate actions to safeguard the PAC.
IoT technology revolutionizes poultry farming by enabling real-time data collection and analysis. Traditional manual methods for monitoring temperature, humidity, and AC voltage are being replaced with automated systems. The IoT setup includes three sensor nodes, CCTV, an IoT gateway, and a web server. Temperature ranges from 27 to 35°C in offfattening periods and consistently above 30°C during fattening. Humidity fluctuates between 60% to 90% in both periods. The CPU temperature remains within safe limits. Uplink data rates exceed 2 Mbps, while AC voltage initially falls below standards but improves over time.
Microservice architecture has become the design paradigm for creating scalable and maintainable software systems. Selecting the proper communication protocol in microservices is critical to achieving optimal system performance. This study compares the performance of three commonly used API protocols: REST, GraphQL, and gRPC, in microservices architecture. In this study, we established three microservices implemented in three containers and each microservice contained a Redis and MySQL database. We evaluated the performance of these API protocols using two key performance metrics: response time and CPU Utilization. This study performs two distinct data retrieval: fetching flat data and fetching nested data, with a number of requests ranging from 100 to 500 requests. The experimental results indicate that gRPC has a faster response time, followed by REST and GraphQL. Moreover, GraphQL shows higher CPU Utilization compared to gRPC and REST. The experimental results provide insight for developers and architects seeking to optimize their microservices communication protocols for specific use cases and workloads.
Instantaneous frequency measurement devices are designated for very fast measurements of the current frequency value of microwave signals, even if they are very short in the time domain. Fast measurements of frequency temporary values may be based on the evaluation of the phase difference of signal propagating through the microwave transmission lines with unequal, but known, lengths. This paper presents the principle of determination of temporary values of the microwave signal frequency using the digitalized signals and the binary value of them eventually. In the purpose of increase the frequency discrimination resolution, additional tracks with lines with a larger length are proposed. For the system with elements with analytical model transmission characteristics it is typical that bands of ambiguity of frequency measurement occurs. To tackle this problem in addition to 4 x 4 Butler matrix implementation the method of using combination sine and cosine signals is proposed.
Distributed Mode Loudspeakers (DMLs), as other electroacoustic transducers with electrodynamic excitation, are susceptible to the occurrence of nonlinear distortion. The literature on nonlinear distortion in DMLs is scarce. The vibrations of the DML panel are very small in amplitude, thus the coil excursion of the exciter is low, which is a favorable condition for low nonlinear distortion. The scope of this paper covers investigation of harmonic and intermodulation distortion, occurring in the DML. The harmonic distortion was examined with two methods and the intermodulation distortion – with three, including one author’s proposed method. The result indicate, that nonlinear distortion in DMLs may be a bigger problem than those in diaphragm loudspeakers.
Intrusion Detection System and SMS controller with Snort using SMS Gateway on public networks. The need for an attack detection system that can assist administrators in monitoring the network, so that administrators can cope with threats quickly and the network can operate optimally again, the purpose of this study is to facilitate network administrators in preventing and detecting attacks. this has been tested against ping of death attacks, sql injection, sysflooding attacks and port scanning. From the results of testing the system that has been tested, the system has been able to detect and prevent it quickly via SMS controller integrated with gammu as an sms gateway, as for information sent to the network administrator in the form of attack alerts in real time via SMS, the result of attack detection in the form of 4 (four) types of information, namely attack time, attack type, destination IP, and source IP. Blocking attacks is done by sending blocking commands via an sms controller that is integrated with php scripts, bash scripting and iptables, blocking attacks successfully executed after the gammu server receives an SMS reply from the network administrator in an average period of 40 seconds.
This paper mainly concentrates and discusses on sugarcane crop, the variety of cane seeds available for sowing; various cane diseases and its early detection using different approaches. Machine Learning (ML) and Deep Learning (DL) techniques are used to analyze agricultural data like temperature, soil quality, yield prediction, selling price forecasts, etc. and avoid crop damage from a variety of sources, including diseases. In the proposed work, with particular reference to eight specific sugarcane crop diseases and including healthy crop database, the neural network algorithms are tested and verified in terms quality metrics like accuracy, F1 score, recall and precision.
The Internet of Things (IoT) has experienced significant growth and plays a crucial role in daily activities. However, along with its development, IoT is very vulnerable to attacks and raises concerns for users. The Intrusion Detection System (IDS) operates efficiently to detect and identify suspicious activities within the network. The primary source of attacks originates from external sources, specifi-cally from the internet attempting to transmit data to the host network. IDS can identify unknown attacks from network traffic and has become one of the most effective network security. Classification is used to distinguish between normal class and attacks in binary classification problem. As a result, there is a rise in the false positive rates and a decrease in the detection accuracy during the model's training. Based on the test results using the ensemble technique with the ensemble learning XGBoost and LightGBM algorithm, it can be concluded that both binary classification problems can be solved. The results using these ensemble learning algorithms on the ToN IoT Dataset, where binary classification has been performed by combining multiple devices into one, have demonstrated improved accuracy. Moreover, this ensemble approach ensures a more even distribution of accuracy across each device, surpassing the findings of previous research.
Faculty of Computer Sciences, Universitas Muhammadiyah Riau, Pekanbaru, Riau Indonesia and Faculty of Data Science and Computing, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia
Faculty of Data Science and Computing, Universiti Malaysia Kelantan and Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia
Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia
Universitas Muhammadiyah Riau, Pekanbaru, Riau Indonesia and Faculty of Data Science and Computing, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia
Heart abnormalities are atypical heart conditions that can lead to chronic heart disease. Heart abnormalities can be severe if not treated directly due to the crucial function of the heart as the blood circulation center. Heart abnormalities cannot be seen with the naked eye so it requires the recording of a heartbeat wave or electrocardiogram (EKG) for the disease to be detected. Therefore, a strategy that uses image processing and artificial neural networks to detect anomalies in the heart is strongly advocated. The proposed methods for feature extraction and identification are Invariant Moments and Extreme Learning Machine respectively. The testing procedure for this research employed a total of 386 ECG images as training data. and 44 ECG images for test data, and the heart condition was classified into 4 classes, namely Atrial Fibrillation, T-Wave, ST-Segment, and normal heart conditions. The test was carried out using 3 choices of extreme learning machine activation functions, namely sigmoidal, sine and hard-lim. The test also applied the parameter of hidden neurons in which amounting to 10, 30, 50, 100 and 500. The system accuracy in identifying heart abnormalities achieved 95.45% by the application of the sigmoid function with the total number of hidden neurons equal to 500.
DNA, a significant physiological biometric, is present in all human cells like hair, blood, and skin. This research introduces a new approach called the Deep DNA Learning Network (DDLN) for person identification based on their DNA. This novel Machine Learning model is designed to gather DNA chromosomes from an individual’s parents. The model’s flexibility allows it to expand or contract and has the capability to determine one or both parents of an individual using the provided chromosomes. Notably, the DDLN model offers quick training in comparison to traditional deep learning methods. The study employs two real datasets from Iraq: the Real Iraqi Dataset for Kurds (RIDK) and the Real Iraqi Dataset for Arabs (RIDA). The outcomes demonstrate that the proposed DDLN model achieves an Equal Error Rate (EER) of 0 for both datasets, indicating highly accurate performance.
The ever-growing deluge of astronomical data challenges traditional server-based processing, hindering real-time analysis and scientific discovery. This paper proposes a novel approach: edge computing directly on an sCMOS camera using a System-on-Chip (SoC) architecture currently developed at Creotech Instruments. We present a custom-designed camera equipped with an FPGA-based SoC, enabling on-board preprocessing and feature extraction of astronomical images. This significantly reduces data transmission, minimizes latency, and empowers real-time decision-making for critical observations. We showcase the camera's capabilities through real-world scenarios, demonstrating its usability in astronomy.
The article presents problems related to the design of sequential control systems using algorithmic design method. Based on a graph describing the functions of signal processing, the method of fast programming of sequential electro-pneumatic systems and systems with logic elements is presented. The developed sequential system was verified through simulation using the FluidSim computer-aided design software from Festo.
First sections of the paper contain some considerations relevant to the reversibility of quantum gates. The Solovay-Kitayev theorem shows that using proper set of quantum gates one can build a quantum version of the nondeterministic Turing machine. On the other hand the Gottesmann-Knill theorem shows the possibility to simulate the quantum machine consisting of only Clifford/Pauli group of gates. This paper presents also an original method of designing the reversible functions. This method is intended for the most popular gate set with three types of gates CNT (Control, NOT and Toffoli). The presented algorithm leads to cascade with minimal number CNT gates. This solution is called optimal reversible circuits. The paper is organized as follows. Section 5 recalls basic concepts of reversible logic. Section 6 contain short description of CNT set of the reversible gates. In Section 7 is presented form of result of designing as the cascade of gates. Section 8 describes the algorithm and section 9 simple example.
The article is part of a course on Quantum Information Technologies QIT conducted at the Faculty of Electronics and Information Technology of the Warsaw University of Technology. The subject includes a publishing workshop exercised by engineering students. How do ICT engineers see QIT from their point of view? How can they implement quantum technologies in their future work? M.Sc. students usually have strictly declared topics for their master’s theses. The implementation of some works is at an advanced stage. The potential areas of application of QIT are defined and narrow if they are to intellectually expand the area of the completed theses. This is the idea of incorporating QIT components or interfaces into classic ICT solutions at the software and hardware level. It is possible to propose a solution in the form of a functional hybrid system. QIT systems should be functionally incorporated into the existing ICT environment, generating measurable added value. Such a task is quite demanding, but practice shows that it interests students. Solutions don’t have to be mature or even feasible. They can be dreams of young engineers. The exercise is a publication workshop related to the fast development of QIT. The article is a continuation of publication exercises conducted with previous groups of students participating in QIT lectures.
The miniaturization of substrate-integrated waveguide (SIW) antenna suffers from the narrow impedance bandwidth. It occurs on the quarter mode substrate integrated waveguide (QMSIW) antenna that has 75% miniaturization of the full mode SIW. This research proposed the bandwidth enhancement for QMSIW antenna by using dual cavity and triangle slot. The QMSIW antenna feeds in a single port. The impedance bandwidth simulation has an 8.6% fractional bandwidth improved with dual resonant frequencies. The simulation result was validated with the measured impedance bandwidth.
Today age of advancement one of the fastest growing fields of the technology is wearable electronics and device. In the recent advancement the wearable devices for on and off body communication is going expeditiously. For the wearable wireless communication, wearable antennas are mostly used due to its compact size, self powered, light weight, low profile, portable wireless communication and sensing. This paper throws light on wearable antennas for on body and off body communication including their applications, advantages and disadvantages. A comparative study is conducted on designing of different on body and off body wearable antennas and parameters of designed antenna such as their size, shape, gain, SAR have been compared and analyzed. In this paper also discussed the impact of the wearable antenna on human body and impact of human body on antenna.
Synovitis is the inflammation of a synovial membrane surrounding a joint. Its assessment is an important step in the diagnosis and treatment of rheumatoid arthritis. Joint detection is the first stage of an automated method of assessment of a degree of synovitis, from an Ultrasound (USG) image of a finger joint and its surrounding area. A joint detector consists of three parts: image preprocessing, feature extraction, and classification. Each part contains adjustable parameters that must be set experimentally to ensure the proper operation of the detector. Both the structure of a joint detector and a procedure for finding a near-optimal configuration of the adjustable parameters are described. The optimization process is based on two evaluation measures: Area Under the Receiver Operating Characteristic Curve (AUC) and False Positive Count (FPC). The optimization process decreases the number of pictures with multiple detections, which was the main point of works presented in this paper. This was achieved by increasing the number of components of the homogeneous mixed-SURF descriptor which has the greatest influence on the final result. Non-SURF descriptors achieve poorer classification results. Our research led to the creation of a better joint detector which could positively influence the final results of inflammation level classification.
Abstract—The prevalence of dementia is expected to increment in the next decades as the elderly population grows and ages. Hence, Alzheimer’s Disease (AD), as the most frequent dementia, will be more problematic from a socioeconomic point of view. Different diagnostic criteria have been proposed by clinicians for the early diagnosis of AD. After discarding the longitudinal and prognosis articles, a selection of articles from the last decade and based on Artificial Neural Networks (ANNs) was collated from the PubMed database, and complemented with researches extracted from others. The latest trends on this field were discovered in these selected articles, which were later discussed. Only articles based whether on shallow ANNs, Deep Learning (DL) or a mix of both were included. The total number of cross-sectional articles that complied with our selection criteria was 154. Convolutional Neural Networks (CNNs) combined with neuroimaging has been the most popular approach, yielding very good performance results. Approaches based on nonneuroimaging techniques, such as gait, genetics, speech and neuropsychological tests, were less common but have their own advantages. Multimodality solutions may become even more prevalent in the near future. Similarly, novel diagnostic criteria will appear and the popularity of currently not-so-common ones will expand. A new proposal emerged from these trends, which is based on ontogenetic ANNs.
Instituto Universitario de Cibernetica, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Parque Cientıfico Tecnologico, Campus Universitario de Tafira, Las Palmas de Gran Canaria, CN, Spain
Departamento de Ingenierıa Informatica y de Sistemas, Universidad de La Laguna, Escuela Superior de Ingenierıa y Tecnologıa, San Cristobal de La Laguna, CN, Spain
Campylobacteriosis is the most common acute bacterial diarrheal disease in our population. It is caused by bacteria of the genus Campylobacter species whose prevalence in the environment and ease of transmission make these infections a serious epidemiological problem. Although the disease usually has a picture of mild self-limiting diarrhea in some cases there is a more severe course with the need for hospital care. Colonization by Campylobacter spp. also plays on of the main role in the pathogenesis of other diseases. The study was conducted using data from the records of 67 patients aged 3 months to 10 years hospitalized for acute diarrheal illness caused by Campylobacter spp. Microbiological culture yielded growth of C. coli in 14 cases and C. jejuni in 52 patients. The isolated pathogens showed significant antibiotic resistance variable depending on the bacterial strain. The least susceptibility to the drugs occurred with erythromycin and was mainly related to C jejuni. In 42 children it was necessary to implement antibiotic therapy during which azithromycin, amoxicillin with clavulanic acid, or Biseptol were used.
3rd Department and Clinic of Paediatrics, Immunology and Rheumatology of Developmental Age, Wroclaw Medical University, Wroclaw, Poland; Department of Immunology and Paediatrics, Provincial Hospital J. Gromkowski, Wroclaw, Poland
Department of Paediatrics, Provincial Hospital J. Gromkowski, Wroclaw
Department of Surgery, Wroclaw University of Environmental and Life Sciences, Wroclaw, Poland
With the development of the entertainment industry, the need for immersive and emotionally impactful sound design has emerged. Utilization of spatial sound is potentially the next step to improve the audio experiences for listeners in terms of their emotional engagement. Hence, the relationship between spatial audio characteristics and emotional responses of the listeners has been the main focus of several recent studies. This paper provides a systematic overview of the above reports, including the analysis of commonly utilized methodology and technology. The survey was undertaken using four literature repositories, namely, Google Scholar, Scopus, IEEE Xplore, and AES E-Library. The overviewed papers were selected according to the empirical validity and quality of the reported studies. According to the survey outcomes, there is growing evidence of a positive influence of the selected spatial audio characteristics on the listeners’ affective responses. However, more data is required to build reliable, universal, and useful models explaining the above relationship. Furthermore, the two research trends on this topic were identified. Namely, the studies undertaken so far can be classified as either technology-oriented or technology-agnostic, depending on the research questions or experimental factors examined. Prospective future research directions regarding this topic are identified and discussed. They include better utilization of scene-based paradigms, affective computing techniques, and exploring the emotional effects of dynamic changes in spatial audio scenes.
The article describes the design and implementation of a modular hardware platform intended for testing and measuring various configurations of switching-mode audio amplifiers based on sigma-delta modulation. Two single-channel power amplifier modules were designed and manufactured, along with a stereophonic module serving as the basic source of modulated signals. Additionally, measurements were conducted on the fundamental parameters of the completed amplifier, such as harmonic distortion level, dynamic range, and output power. The developed platform serves as a foundation for modifications and further advancement in the technology of building switching-mode audio amplifiers.