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The solar photovoltaic (PV) power generation industry has experienced substantial, ongoing growth over the past decades as a clean, cost-effective energy source. As electric grids use ever-larger proportions of solar PV, the technology’s inherent variability—primarily due to clouds—poses a challenge to maintaining grid stability. This is especially true for geographically dense, electrically isolated grids common…

Cybercriminal exploits integrity, confidentiality, and availability of information resources. Cyberattacks are typically invisible to the naked eye, even though they target a wide range of our digital assets, such as internet-connected smart devices, computers, and networking devices. Implementing network anomaly detection proves to be an effective method for identifying these malicious activities. The traditional anomaly…

In conjunction with psychological stress, physiological indicators such as heart rate (HR) and heart rate variability (HRV) are frequently employed. This study uses a substitute for heart rate variability (HRV) known as short-term pulse rate variability (PRV) to evaluate emotional changes. We examined sixteen college students using a low-cost photoplethysmograph and obtained a short-term PRV…

Many consequences can be resulted in mismanagement of crowd, which might get people injured or even lose their lives. Thus, crowd management helps in controlling overcrowded areas during events, and allowing authorities to monitor, manage and reduce incidents. Space science and technology have made huge leap in crowd management, let alone when this technology integrated…

In view of the current short-term traffic flow prediction methods that fail to fully consider the spatial correlation of traffic flow, and fail to make full use of historical data features, resulting in low prediction accuracy and poor robustness. Therefore, in paper, combining Non-negative Matrix Factorization (NMF) and LSTM model Based on Attention Mechanism (AttLSTM),…

The new model of the short-term exchange rate dynamics was constructed. First of all, the most interesting were the reasons of the deviation from the medium-term equilibrium. The author’s IFEER-concept (International Flows Equilibrium Exchange Rate) was used as a base and it was developed. In this study, due to the short-term modeling period the differential…

The paper discusses the features of the implementation and functioning of digital self-timed circuits. They have a naturally high tolerance to short-term single soft errors caused by various factors, such as nuclear particles, radiation, and others. Combinational self-timed circuits using dual-rail coding of signals are naturally immune to 91% of typical soft errors classified in…

Short-term load forecasting (STLF) plays an important role in building business strategies, ensuring reliability and safe operation for any electrical system. There are many different methods, including: regression models, time series, neural networks, expert systems, fuzzy logic, machine learning and statistical algorithms used for short-term forecasts. However, the practical requirement is how to minimize the…

As society ages, the imbalance between family caregivers and elderly individuals increases, leading to inadequate support for seniors in many regions. This situation has ignited interest in automatic health monitoring systems, particularly in fall detection, due to the significant health risks that falls pose to older adults. This research presents a vision-based fall detection system…

Forecasting solar PV power output holds significant importance in the realm of energy management, particularly due to the intermittent nature of solar irradiation. Currently, most forecasting studies employ statistical methods. However, deep learning models have the potential for better forecasting. This study utilises Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU) and hybrid LSTM-GRU deep…

Machine Translation (MT) has come a long way toward reducing linguistic gaps. However, its progress in efficiently handling low-resource languages—such as the Wa language in the Myanmar-Wa corpus—has not received enough attention. This study begins with a thorough investigation of the historical development of MT systems, painstakingly following their development against the complex background of…

Over the past decade, Bitcoin’s unprecedented performance has underscored its po-sition as the premier asset class. Starting from an insignificant value and reaching an astounding high of around 65,000 U.S dollars in 2021 – all without a central con-trolling authority – Bitcoin’s trajectory is undoubtedly a historical feat. Its intangible nature, initially a subject of…

Sleep stage performs a vital role in people’s daily lives in the detection of sleep-related diseases. Conventional automated sleep stage classifier models are not efficient due to the complexity linked to the design of mathematical models and extraction of hand-engineering features. Further, quick oscillations amongst sleep stages frequently lead to indistinct feature extraction, which might…

Drought has become more common and severe in recent decades due to increased global changes in climatic conditions and anthropogenic activities thereby analyzing drought at both regional and local levels is crucial. And so, this study aims at assessing drought risk in Northern Nigeria using NDVI and SPI. Data used included satellite images (Landsat OLI-TIRS,…

Internet addiction (IA) has adverse effects on psychophysiological responses, interpersonal relationships, and academic and occupational performance. IA detection has received increasing attention. Although questionnaires enable long-term assessment (over 6 months) and physiological measurements to aid the short-term evaluation (over 2 min) of IA, the lack of algorithms results in an inability to detect IA in…

The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques. Due to the increasing large amounts historical data of agricultural commodity prices and the need of performing accurate prediction of price fluctuations, the solution has largely shifted from statistical methods to machine learning…

With the advent of internet technology and social media, patterns of social communication in daily lives have changed whereby people use different social networking platforms. Microblog is a new platform for sharing opinions by means of emblematic expressions, which has become a resource for research on emotion analysis. Recognition of emotion from microblogs (REM) is…

Environmental sounds detection plays an increasing role in computer science and robotics as it simulates the human faculty of hearing. It is applied in environment research, monitoring and protection, by allowing investigation of natural reserves, and showing potential risks of damage that can be deduced from the environmental acoustic. The research presented in this paper…

Forecasting the gold price movement’s volatility has essential applications in areas such as risk management, options pricing, and asset allocation. The multivariate model is expected to generate more accurate forecasts than univariate models in time series data like gold prices. Multivariate analysis is based on observation and analysis of more than one statistical variable at…

A very large number of workers is occupationally exposed to Optical Radiation (OR) worldwide, while indeed nowadays an exposure to Electromagnetic fields (EMF) can occur in almost all workplaces. OR origin can be natural, including the most relevant source, i.e. the sun, or artificial, that can be further classified in incoherent and coherent, i.e. the…

Solar energy becomes widely used in the global power grid. Therefore, enhancing the accuracy of solar energy predictions is essential for the efficient planning, managing and operating of power systems. To minimize the negatives impacts of photovoltaics on electricity and energy systems, an approach to highly accurate and advanced forecasting is urgently needed. In this…

Indoor positioning system (IPS) has become a high demand research field to be developed and has made considerable progress in recent years. Wi-Fi fingerprinting is the most promising technique that produces an acceptable result. However, despite the large amount of research that has been done using Wi-Fi fingerprinting, only a few Wi-Fi based IPS in…

A new method of analysis and classification of rock acoustic emission signals is proposed. It is based on symbol description of signals and involves the following processing. First, signal segments containing pulses are detected. Second, noise of the detected pulses is reduced by the wavelet filtration method. Fourth-order symlets and adaptive threshold scheme based on…

In this paper we propose the application of supervised learning techniques to recognize stress situations in drivers by analyzing their Skin Potential Response (SPR) and the Electrocardiogram (ECG). A sensing device is used to acquire the SPR from both hands of the drivers, and the ECG from their chest. We also consider a motion artifact…

Malware has always been a big problem for companies, government agencies, and individuals because people still use it as a primary tool to influence networks, applications, and computer operating systems to gain unilateral benefits. Until now, malware detection with heuristic and signature-based methods are still struggling to keep up with the evolution of malware. Machine…