In the era of data-driven technologies, need for diverse and high-quality datasets training testing machine learning models has become increasingly critical. this article, we present a versatile methodology, Generic Methodology Constructing Synthetic Data Generation (GeMSyD), which addresses challenge synthetic data creation in context smart devices. GeMSyD provides framework that enables generation datasets, aligning them closely with real-world data. To demonstrate utility GeMSyD,...

The electroencephalography (EEG) data records vast amounts of human cerebral activity yet is still reviewed primarily by readers. Most the times, contaminated with non-cerebral originated signals, called artifacts, which could be very difficult to visually detect and, undiscovered, damage neural information analysis. purpose our work artifacts identifying most relevant features, both in temporal and frequency domains, train various supervised learning algorithms: Decision Trees, SVM KNN,...

Smart cities facilitate the comprehensive management and operation of urban data generated within a city, establishing foundation for smart services addressing diverse challenges. A system public laundry uses artificial intelligence-based solutions to solve challenges inefficient utilization laundries, waiting times, overbooking or underutilization machines, balancing loads across implementation energy-saving features. We propose SmartLaundry, real-time design recommendations better manage...

In the context of current technological progress, big data arises as a compelling research topic. This paper presents non-traditional analysis strategies like exploiting semantics (cycle identification) well traditional ones (signal interpolation and correlation) for industrial within Big Data paradigm. A general approach preprocessing operations exploring extracting valuable knowledge from large set is defined. The identified are tested validated on real characterized by multitude...

In the current technological context where signals can assist functionality of engines in operation and correct be monitored. Therefore, patterns utilization identified for predictive preventive maintenance such engines, thus predicting Remaining Useful Life (RUL). For this reason, developing strategies to extract knowledge from recorded preventing flaws is necessary it opens an entire research direction. This paper presents development a generic strategy exploring, analyzing value RUL...

Due to technological advances, massive amount of data is recorded in all areas. Not only the manufacturing processes are monitored and can benefit from augmentation, but also utilisation products produce large amounts information rich data. With right set skills processing strategies, valuable process-related be extracted such However, before applying any complex tasks as user profiling, raw needs preprocessed, knowledge extracted. In this paper, we propose a steps for extraction home...

Data collected by sensors has hidden value that can be used to infer valuable knowledge about the system, such as identifying faults in transmission or functioning various system components. Solutions for exploring and exploiting data need developed extract knowledge. This paper shows how identification of regularities overall state.The focus this work is defining a methodology detecting periodicity. In our approach, we evaluated other strategies, addressed limitations they have, narrowed...

This paper presents a novel approach for electricity consumption profiling in households through the fusion of usage data individual smart devices. The novelty consists leveraging representing appliances rather than using direct measurements energy consumption. Our methodology focuses on merging signals interaction user with device to compute patterns total per household. Subsequently, we apply mining techniques—specifically, unsupervised clustering—to analyze resulting time-series daily...

In light of the energy crisis, extensive research is being conducted to enhance load forecasting, optimize targeting demand response programs, and advise building occupants on actions performance. Cluster analysis increasingly applied usage data across all consumer types. More accurate identification translates improved resource planning. context Industry 4.0, where comprehensive are collected various domains, we propose using existing sensor from household appliances extract patterns...

The business value embedded in the IoT devices data is estimated as precious difficulty to seize it. Even though costs of accumulating and analysing this information frequently outweigh returned avail, scope described solution overcome these complexities. modernisation that arose around Industry 4.0 context has generated demand for a competing system competent streamlining methods mechanisms big administration. system's flexibility adaptability are essential characteristics such progressive...

Machine learning has triggered a paradigm shift, facilitating the gathering of relevant information from large amount seemingly diverse data. The primary goal this research paper is to emphasize effectiveness varied set data analysis methods used extract meaningful intelligence complex gathered by experimenting with house-hold appliances in controlled environment. differ depending on and characteristics data: machine learning, statistical modeling, natural language processing. In paper, we...