Earticle

The identification of key feature selection plays a significant role in accurate population projection, which is an essential aspect of demographic statistics. The goal of this paper is to investigate the importance of the different features in population projection by using four advanced feature analysis techniques i.e. Canonical Correlation Analysis (CCA), Linear Discriminant Analysis (LDA) Fast Independent Component Analysis (FICA) and Principal Component Analysis (PCA). This analysis is important to determine the major factors that affect population change. The identification and ranking of these predictors can enhance demographic forecasting and policy planning. We utilized Koran population data from the UN Population Division dataset and evaluated the above four methods. The experimental results reveal that LDA achieved the lowest performance in selecting the most appropriate features, while PCA is the most efficient in selecting an effective feature with the highest variance. These insights build up the knowledge of population change and refine the projection models.

The battery market is experiencing rapid growth due to advancements in technology and increased recycling efforts. Verifying the suitability of developed batteries through rate capability experiments, which measure capacity based on charging and discharging speeds, is essential but resource-intensive and time-consuming. This research proposes a method to predict battery rate capability using a polynomial regression model based on similar data groups, aiming to shorten these experiments. The research was conducted in two main stages, namely the construction of the dataset and the development of the predictive model. Data was collected from experimental graphs in existing literature and new experiments on Coin Cell batteries. Through preprocessing steps including deduplication, interpolation, and extrapolation, a comprehensive dataset was created. A combined Quadratic and Linear Piecewise Interpolation method was developed to handle missing data efficiently. In the model development stage, polynomial regression models were created for groups of similar battery data, allowing accurate predictions for partial rate capability experiments. Experimental results demonstrated high accuracy, significantly reducing the need for extensive testing. The proposed method offers substantial time and resource savings, enhancing the efficiency of the battery development process.

Recognizing anomalies in surveillance is crucial for public safety to identify events that deviate from normal patterns. Visual information is essential for effective anomaly recognition; however, audio data can enhance recognition accuracy by providing additional context. Despite this, existing systems only utilize visual information, overlooking the potential of audio modalities in anomaly recognition. This paper introduces a multi-modal framework for anomaly recognition through active learning, integrating audio and visual modalities to enhance anomaly prediction. The framework extracts features using a pretrained ResNet-50 convolutional neural network (CNN) model from the visual and audio data. The extracted features are then forwarded to the Bi-Directional Long Short-Term Memory (Bi-LSTM) network for temporal feature learning. These features are then fused and fed into a classification layer for final prediction. The proposed framework's performance is assessed on a benchmark dataset and yields promising results.

In recent years, anomaly recognition using audio has attracted the attention of the research community, due to the increasing number of abnormal situations day by day. In the past, researchers have mainly focused on video-based anomaly recognition. However, occlusion is one of the most important factors due to which the anomalous object is unidentifiable. Therefore, in this paper, we proposed a modified vision transformer that utilized the Shifted Patch Tokenization (SPT), and Local Self-Attention (LSA) mechanism and reduced the number of multilayer perceptrons in the head, enabling the model to capture rich spatial information within the spectrogram of anomalous data. The proposed model is implemented using the Sound Events for Surveillance Applications (SESA) dataset and obtained 87% testing accuracy. Thus, the proposed model is an efficient and effective solution for audio-based anomaly recognition.

Nowadays, surveillance systems play a pivotal role in monitoring various sectors to ensure public safety and security. These systems generate massive amounts of video data. Therefore, effective analysis of these streams is an important research area with multiple applications. Several methods have been reported for the automatic recognition of abnormal activities, but these techniques show limited performance while learning complex temporal dependencies of real-world surveillance of abnormal activities. We introduce a Deep Learning (DL)-assisted framework that is mainly divided into two parts. First, the surveillance video stream is preprocessed, and then the BoTNeT-152 is employed to extract spatial features. Secondly, a Residual Deep Bidirectional Long Short-Term Memory (RBLSMT) Network is introduced to learn the complex temporal dependencies across multiple frames for abnormal activity recognition. To assess the effectiveness of our proposed method, we evaluated its performance on the benchmark real-world UCFCrime2Local dataset, achieving an accuracy of 86% reveals a significant improvement of up to 2% compared to existing methods which shows the superiority of the suggested technique in addressing the challenges posed by complex surveillance environments.

This paper introduces a comprehensive approach to dataset standardization aimed at enhancing the effectiveness and reliability of solar power forecasting models. Leveraging multiple datasets, this study incorporates additional attributes such as atmospheric pressure and sunshine duration. These enrichments bridge critical gaps in meteorological and environmental data, facilitating more robust and precise solar power forecasting. The paper underscores the significance of these attributes, furnishes detailed equations for their computation, and presents the outcomes of their integration. It underscores their pivotal role in enabling solar energy stakeholders to make informed decisions and optimize energy production effectively.

Sustainable power systems should include solar energy generation. However, for effective grid management and the integration of renewable energy sources, accurate solar power generation predictions are essential. Therefore, this study compares the prediction of solar power forecasting in Italy and Bulgaria. These are two countries that have alike latitudes but different populations and solar energy production. The historical solar power generation and meteorological data from these countries are preprocessed and then used to apply four different deep learning models including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results are analyzed to gain insights into how the proximity of geographical locations and the quality and quantity of data impact the precision of prediction algorithms.

A crucial component of designing intelligent and ecologically friendly environments nowadays is electricity consumption forecasting. The generation of energy can be enhanced to effectively meet the population's rising requirements by using the prediction of future electricity consumption. Due to the broad variety of consumption patterns, it is difficult to anticipate the energy requirements of buildings. Therefore, this work uses a dual-steam approach with multi-head attention to anticipate the power consumption of the building to address this issue and produce precise predictions. The proposed network concurrently learns temporal representations through a Bidirectional Gated Recurrent Unit (BGRU) and spatial patterns through Atrous Convolutional Neural Network (ACNN). The obtained features are combined to create a single feature vector that is used as the input for the multi-head attention, which finds the features that are most suited to forecasting the electricity consumption of a building. Finally, the dense layer receives the effective features and uses them to forecast short-term power consumption. In this paper, the proposed dual-stream network with attention outperforms competing models, achieving the lowest error value for hourly building power consumption prediction, according to experimentation on the household electricity consumption dataset.

Nowadays, energy management and its optimization using smart devices are getting more attention due to their significant applications. Moreover, the applications used in these devices play a key role in developing smart cities that is only the way to solve urban problems. The potential of renewable energy sources like solar and wind power has been integrated in the smart grids to overcome the lack of supply via conventional fossil fuels and their environmental disputes that reduce operational cost. This review paper describes the significance of renewable power data that directly assists all the functions in smart cities such as the evolution of microgrids, renewable resources, energy forecasting, and power storage technologies. Furthermore, solar and wind power plants’ data with weather information as an additional cue is collected from different companies in South Korea. We aim to assist the researchers to develop artificial intelligence (AI)-based algorithms for power forecasting and establish its efficient management between suppliers and consumers.

The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power challenges the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate prediction for power generation is important to provide balanced electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual stream Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to learn spatial patterns using CNN and temporal features via the LSTM network. These features are then fused via a concatenation layer and then feed forward to Dense layers for optimal features selection and future solar power prediction. The performance of the proposed model is evaluated on benchmark datasets and achieved a new state-of-the-art on these datasets.

Unmanned aerial vehicles (UAVs) or drones are versatile innovations that can capture pictures and videos and even collect air or soil samples. Natural disaster drones are especially critical, which help with understanding the damage after a disaster, locating people who need help, distributing resources and preparing for the next event. Computer vision, deep learning (DL), and drones can augment the existing sensors, thereby increasing the accuracy of natural disasters detector, and most importantly, allow people to take precautions, stay safe, and reduce the number of deaths and injuries that happens due to these disasters. Therefore, in this paper we propose a novel lightweight convolutional neural network (CNN) based framework to detect natural disasters including cyclone, flood, earthquake, and wildfire. The proposed CNN model is obtained by fine-tuning the MobileNetV2 that can be deployed on drones. Furthermore, the model is trained and evaluated using a publicly available natural disasters dataset by obtaining 83.4% accuracy. Similarly, the framework has ability to broad cast the notification in alarming situations, which makes our proposed framework a best fit for natural disasters detection in realworld surveillance settings.

Renewable energies use clean sources for energy generation and have the potential to balance the supply and demand of power. One of the best ways to save energy for high-demand time is to preserve it in a battery energy storage system (BESS). Various methods are presented in the last two decades for battery state of charge (SOC) estimation, however, most of them are focused only on a single battery pack and use data without accurate preprocessing and feature selection strategy. Therefore, in this paper, we conduct a comparative analysis of machine learning (ML) models with a specific preprocessing strategy and suggest a high performer model for battery rack SOC estimation. First, we preprocess the data by cleaning, normalizing, selecting important attributes, and then split it into training and testing sets. Next, four ML models are trained using the training data for SOC estimation, and finally, for better evaluation, each model is evaluated on the testing data using various error metrics. After comprehensive experiments, we suggest multilayer perceptron (MLP) due to high performance for batteries rack SOC estimation.

Fire is an extremely catastrophic disaster that leads to the destruction of forests, human assets, reduced soil fertility, land resources, and the cause of global warming. In the current decade, fire detection and its management are the major concern of several researchers to prevent social, ecological, and economic damages. To overcome such kind of losses, early fire detection, and the automatic response is very significant. Moreover, achieving high accuracy with reducing inference time and model size is also challenging for the Unmanned Aerial Vehicle (UAVs). Therefore, in this work, we enabled the VGG16 architecture for UAV in terms of reducing its learning parameters from 138 million to 11.4 million for early fire detection. The proposed system is inexpensive in terms of computation and size. The performance of our proposed work is evaluated over the custom dataset. We performed comprehensive experiments using various deep learning architectures such as VGG16, ResNet50, and the proposed CNN model. The experimental results based on the proposed model achieved an accuracy of 98% on 50 epochs.

인공지능을 활용한 사업이 활발히 진행되면서 범죄 예방 및 안전분야와 관련하여 이상행동 및 행동 인식에 대한 연구와 관심이 높아지고 있다. 하지만 딥러닝 등 인공지능 모델을 생성하는 것은 전문 지식이 없는 경우 많은 어려움이 따른다. 본 논문에서는 사용자가 편리하게 딥러닝 모델을 생성할 수 있도록 데이터셋을 제공하고 이상행동 및 행동 인식 기술을 API화하여 인터페이스에서 호출하는 방식을 사용하는 사용자 친화적인 모델 학습 및 테스트를 위한 시스템 UI를 제안하였다. 본 논문에서 제안한 시스템은 딥러닝에 대한 사전 지식이 없는 사용자가 편리하게 딥러닝 모델을 생성할 수 있을 것으로 기대된다.

Forest fire is one of the most dangerous disasters worldwide, due to which its management is a key concern of the research community to prevent social, ecological, and economic damages. Wildfires are extremely catastrophic disasters that lead to the destruction of forests, human assets, reduction of soil fertility and cause global warming. To overcome such kind of losses early fire detection and quick response is the key concern of research community. Therefore, in this paper, we propose a lightweight convolution neural network (CNN) method to efficiently detect the forest fire for unmanned aerial vehicles (UAVs) or drones. For the experimental evaluations, we develop an aerial images dataset from YouTube, movies, and google images. The results of the proposed architecture reveal its good performance in terms of 96% accuracy.

CNN Convolutional Neural Networks)은 영상 분류, 인식 및 검색 작업에 대한 유망한 결과를 보여주었다. 이 러한 관점에서, 스포츠 비디오 분류는 CNN이 덜 탐구된 능동적이고 도전적인 영역으로 남아 있다. 이에 우리는 새 로운 데이터 세트를 생성하여 스포츠 비디오 분류에 대한 CNN의 경험적 평가를 광범위하게 제공한다. 본 논문에서 는 MobileNetV2 (MbNetV2)네트워크를 이용한 CNN 기반 방법과 스포츠 비디오 분류를 위한 롤링 예측 평균 방법을 제안한다. 제안된 방법은 미세조정된 MbNetV2를 사용하여 비디오의 각 프레임을 분류하고 그 예측을 목록 에 저장한다. 롤링 예측 평균에서 마지막 "K" 예측의 평균이 계산되고 프레임에서 가장 높은 확률 레이블이 할당된 다. 우리는 제안한 방법이 스포츠 데이터 세트에서 97.9%의 최고 정확도를 달성한다는 것을 실험적으로 증명한다.

Convolutional Neural Networks(CNNs) have shown encouraging results for image classification, recognition, and retrieval tasks. In this perspective, the sport videos classification remains an active and challenging area where CNNs are less explored. Encouraged by this, we extensively provide an empirical evaluation of CNNs on sport videos classification by creating a new dataset. In this paper, we propose a CNN based method that uses MobileNetV2(MbNetV2) network and a rolling prediction average method for sport videos classification. The proposed method uses fine-tuned MbNetV2 to classify each frame in the video and stores its prediction in a list. In rolling predition average the mean of last "K" predictions is calculated and assigned the highest probability label to the frame. We experimentally prove that our proposed method achieves the best accuracy of 97.9% on our sport dataset.