Sustainability through digital transformation is essential for contemporary businesses. Embracing sustainability, micro-, small-, and medium-sized enterprises (MSMEs) can gain a competitive advantage, attracting customers investors who share these values. Moreover, incorporating sustainable practices empowers MSMEs to drive innovation, reduce costs, enhance their reputation. This study aims identify how owners or senior managers of initiate project. A systematic literature review was carried...

This study presents a sustainable digital transformation framework to integrate practices into initiatives within Small and Medium Enterprises (SMEs). The methodology includes literature review, creation, case with passive participation. was structured help industries implement responsible digitalization in five key stages: setting objectives, fostering stakeholder-focused engagement, defining objectives dimensions, creating model, executing the project. Validating proposal context of an SME...

The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines research even developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple low computing cost algorithm process an signal. Our approach is based on the use linear regression segment signal, with goal detecting R point wave later, separate signal periods for P, Q, S, T peaks. After pre-processing reduce noise, was able efficiently detect...

With the rapid deployment of Internet Things and cloud computing, it is necessary to enhance authentication protocols reduce attacks security vulnerabilities which affect correct performance applications. In 2019 a new lightweight IoT-based scheme in computing circumstances was proposed. According authors, their protocol secure resists very well-known attacks. However, when we evaluated found some drawbacks, making insecure. Therefore, propose version considering login, mutual key agreement...

Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing low and middle-income countries scarce. We developed a wearable ECG monitor integrated with self-designed wireless sensor signal acquisition. It used native purposely designed smartphone application, based on machine learning techniques, automated classification of captured beats from aged people. When tested 100 older...

Because battery-powered nodes are required in wireless sensor networks and energy consumption represents an important design consideration, alternate sources needed to provide more effective optimal function. The main goal of this work is present harvesting network platform, the Open Wireless Sensor node (WiSe). implementation solar powered platform described including hardware architecture, firmware, a POSIX Real-Time Kernel. A sleep wake up strategy was implemented prolong lifetime...

Machine learning for financial risk prediction has garnered substantial interest in recent decades. However, the class imbalance problem and dilemma of accuracy gain by loss interpretability have yet to be widely studied. Symbolic classifiers emerged as a promising solution forecasting banking failures estimating creditworthiness it addresses while maintaining both interpretability. This paper aims evaluate effectiveness REMED, symbolic classifier, context management, focuses on its ability...

This paper presents a system to classify and locate basic ultrasonic reflectors (plane, corner, edge) in 3-D environments. The classification is based on the principal-component-analysis (PCA) technique, using time of flight (TOF) as parameter. proposes sensorial structure simultaneously obtain up 16 TOFs at every emission/scanning process. A particular different macrosequence has been assigned transducer encode their emissions. These macrosequences, obtained from complementary sets...

Several machine learning approaches have been proposed for classifying electrocardiogram (ECG) signals. Most of these use adaptive filtering techniques to reduce the noise corruption embedded in However, band-pass filters can affect estimation morphological parameters and result misleading interpretation. We propose a noise-tolerant neural network (NN) approach, based on artificial injection, improve generalization capability resulting model. The NN classifier initially discriminated normal...

This paper describes the complete integration of a fuzzy control multiple evaporator systems with IEEE 802.15.4 standard, in which we study several important aspects for this kind system, like detailed analysis end-to-end real-time flows over wireless sensor and actuator networks (WSAN), kernel an earliest deadline first (EDF) scheduler, periodic aperiodic tasking models nodes, lightweight flexible compensation-based algorithms WSAN that exhibit packet dropouts, event-triggered sampling...

In this paper, we addressed the problem of dataset scarcity for task network intrusion detection. Our main contribution was to develop a framework that provides complete process generating traffic datasets based on aggregation real traces. addition, proposed set tools attribute extraction and labeling sessions. A new with botnet generated by assess our method machine learning algorithms suitable unbalanced data. The performance classifiers evaluated in terms macro-averages F1-score (0.97)...

This research challenges assumptions about cybersecurity risk factors, revealing that age, gender, and educational background are not significant determinants of employee susceptibility. It highlights the importance inclusive training programs cater to individuals all age groups, dispelling misconception older employees inherently less tech-savvy more susceptible threats. The findings show teams within organizations significantly impact adoption security policies data handling practices...

Heart diseases rank among the most fatal health concerns globally, with majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures heart's electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These demand specialized algorithms low computational complexity accommodate memory power...

Millions of electrocardiograms (ECG) are interpreted every year, requiring specialized training for accurate interpretation. Because automated and classification ECG signals will improve early diagnosis heart condition, several neural network (NN) approaches have been proposed classifying signals. Current strategies a critical step, the preprocessing noise removal, still unsatisfactory. We propose modular NN approach based on artificial injection, to generalization capability resulting...

This research paper addresses the problem of modeling service between heterogeneous intelligent spaces, essentially to share complex services. In last decade, development-focused health care systems in environments have taken great interest, especially through development that proven useful for medical monitoring users. However, these approaches are limited small geographic areas such as a home or hospital. Current methods cooperative coordination multi-agent investigated composing services,...

This paper describes the analysis and modeling of an ultrasonic sensorial structure based on processing algorithm that uses a set macro-sequences correlation techniques for obtaining impulse response transmission channels simultaneously is proposed. The sensory formed by multiple transducers transmitting receiving environment information simultaneously. employs pseudorandom macro-sequence obtained from complementary M sequences (M- CSS) which, auto-correlation cross-correlation functions,...

Resumen.Usando un robot humanoide Nao, proponemos apoyar a niños que tienen una deficiencia de color específico llamado "daltonismo" evita este grupo minoritario esté alerta posibles advertencias visuales en juegos, parques y zoológicos.La relevancia nuestro estudio radica el apoyo situaciones peligro por parte con daltonismo, identificar amenazas colores específicos ayudar los daltonismo entornos visuales, como asociados ciudades inteligentes.

Down syndrome is the most frequent cause of mental disability, presenting similar characteristics among people with this syndrome, them scarce short-term memory capacity, fatiguing attention, language delay, others.In Mexico only 3% children receive education, so in section we propose use a Humanoid robot for future application therapies syndrome.We how to improve your ability work colors and shapes group Syndrome.