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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…

The cybercriminal utilized the skills and freely available tools to breach the networks of internet-connected devices by exploiting confidentiality, integrity, and availability. Network anomaly detection is crucial for ensuring the security of information resources. Detecting abnormal network behavior poses challenges because of the extensive data, imbalanced attack class nature, and the abundance of features in…

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…

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…

This work proposes a new unsupervised learning approach to detect and locate the risks “abnormal event” in video scenes using Faster R-CNN and Bidirectional LSTM autoencoder. The approach proposed in this work is carried out in two steps: In the first step, we used a bidirectional LSTM autoencoder to detect the frames containing risks. In…

Research focused on training and testing of dataset after Optimizing Software Component with the help of deep neural network mechanism. Optimized components are selected for training and testing to improve the accuracy at the time of software selection. Selected components are required to be attuned and accommodating as per requirement. Soft computing mechanism such as…

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…

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…

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),…

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…

ECG analysis is commonly used by medical practitioners and cardiologists for monitoring cardiac health. A high-performance automatic ECG classification system is a challenging area because there is difficulty in detecting and clustering various waveforms in the signal, especially in the manual analysis of electrocardiogram (ECG) signals. In this paper, an accurate (ECG) classification and monitoring…

Sign language is one of the languages which are used to communicate with deaf people. By using it, they can communicate and understand each other. In Indonesia, there are two standards of sign language which are SIBI (Sistem Bahasa Isyarat) and BISINDO (Bahasa Isyarat Indonesia). Deep learning is a model that is used to apply…

Recurrent Neural Networks has been widely used by researchers in the domain of weather prediction. Weather Prediction is forecasting the atmosphere for the future. In this proposed paper, Deep LSTM networks has been implemented which is the variant of RNNs having additional memory block and gates making them capable of remembering long term dependencies. Fifteen…

In recent years, deep learning has been widely used for time series prediction. Deep learning model that is most often used for time series prediction is LSTM. LSTM is widely used because of its excellence in remembering very long sequences. However, doing training on models that use LSTM requires a long time. Trying from one…

Convolutional LSTMs are widely used for spatiotemporal prediction. We study the effect of using different activation functions for two types of units within convolutional LSTM modules, namely gate units and non-gate units. The research provides guidance for choosing the best activation function to use in convolutional LSTMs for video prediction. Moreover, this paper studies the…

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…

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…

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…

In the time past, virtually all the contemporary communication systems depend on distinct source and channel encoding schemes for data transmission. Irrespective of the recorded success of the distinct schemes, the new developed scheme known as joint source channel coding technique has proven to have technically outperformed the conventional schemes. The aim of the study…

An enduring vision of Artificial Intelligence is to build robots that can recognize and learn the visual world and who can speak about it in natural language. Automatic image description generation is a demanding problem in Computer Vision and Natural Language Processing. The applications of image description generation systems are in biomedicine, military, commerce, digital…

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…

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…

Comparing to other types of vehicle accidents, fatality rate of tipped rollover accidents shows significant number. Thus, tripped rollover prevention systems are important in order to keep driver safe. In other hands, different rollover indices are defined to handle the risk. The variable unknown parameters of each index, for instance, current load of the vehicle…

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…

With recent advancements in medical filed, the quantity of healthcare care data is increasing at a faster rate. Medical data classification is considered as a major research topic and numerous research works have been already existed in the literature. Presently, deep learning (DL) models offers an efficient method for developing a dedicated model to determine…