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Situation recognition is an crucial problem in scene understanding, activity understanding, and action reasoning as it provides a structured representation of the main activity depicted in the image. Semantic role labeling is crucial to situation recognition, which is challenging because a single action can have multiple meanings and purposes depending on its context. Understanding images beyond the highlighted actions requires inferences about the context of the scene, the objects, and their role in the captured event. Recently, situation recognition (SR) has been introduced, which jointly derives a collection of the action (activity), meaning-role, and noun (entities) pairs in the form of moving images. To label these frames as action frames, we must assign nouns (entities) to the role based on the content of the observed image. One of the main challenges is managing the complex dependencies between the assigned roles (nouns) and the predicted action, as the correct role assignment often depends on the accuracy of the action prediction. We introduce, RoadSitu, a road situation recognition that involves generating a structured summary of what is happening in a road scenario using an action and the semantic roles played by agents from a video frame. The action can describe a diverse set of situations, and the same agent can play various roles depending on the situation depicted in the video frame. Therefore, a situation recognition model needs to understand the context of each video frame and the visual-linguistic meaning of the semantic roles of that particular frame. One of the main challenges in this work is the complex task of annotating video frames with semantic roles and handling the structured dependencies between the assigned roles (nouns) and the predicted action (activity). Additionally, the sparsity of meaningful semantic information within road scenarios poses further difficulties. To overcome these challenges, we introduce a novel approach where action recognition and noun estimation work together interactively to form structured summaries of each situation. In experiments using a road video dataset obtained from a South Korean company, RoadSitu achieved significant improvements across various performance metrics, with a Top-1 verb accuracy of 43.46%, Top-5 verb accuracy of 72.48%, and value accuracy of 34.21%, outperforming baseline models such as GSRTR and JSL by 2.4% and 3.86% in Top-1 verb accuracy, respectively. These results demonstrate the effectiveness of our model in handling complex road scenarios.

Lithium-ion batteries have notable benefits in their elevated energy power, density, and efficiency. However, the deterioration of capacity remains a prominent concern throughout their usage. Specifically, calculating the remaining capacity is essential for guaranteeing safe operations, which has prompted the creation of accurate capacity estimate models. Battery capacity estimation is one of the critical functions in the Battery Management System (BMS), and battery capacity indicates a battery's maximum storage capability, which is vital for the battery State of Charge (SOC) estimation and lifespan management. In order to increase the accuracy of battery capacity prediction, we provide in this work an improved Extreme Gradient Boosting (XGBoost) model that has been tuned by Random Search hyperparameter tweaking. Our proposed method achieved a R2 value of 0.9931%, a Mean Squared Error (MSE) of 0.0068, and a Root Mean Squared Error (RMSE) of 0.0825 when applied to an extensive dataset. These outcomes indicate significant improvements compared to traditional regression models. Moreover, the suggested technique has yielded superior accuracy and resilience in estimating lithium-ion battery capacity.

Diabetic Retinopathy (DR) is one of the biggest reasons for vision loss. It is a fatal eye disease damaging the retina, which is the light-sensitive tissue in the rear of the eye. Ophthalmologists use fundus images to capture retinal inner structures to find broken blood vessels and scars. To detect DR on time, early diagnosis is very important which is often not possible due to complex procedures. Therefore, automation of DR detection can solve this problem. Accessibility to regular examinations and specialized eye care remains a challenge, especially in underserved areas, due to late diagnosis, a lack of healthcare infrastructure, and other factors. Although automated detection and grading of diabetic retinopathy from retinal images has shown promising results, challenges arise to accomplish high accuracy, particularly in hidden or early-stage DR situations. One of the major limitations in developing a detection model is the lack of imaging datasets as it requires a large number of images to train the model more accurately. Deep transfer learning-based models have shown promising results especially when datasets are not very large. This study used a weighted average ensemble approach to combine three different deep learning models: inception-v3, VGG16, and a custom-built convolutional neural network. The proposed weighted average ensemble approach achieved an accuracy of 95.06%, a precision of 87.88%, a recall of 83.78%, f1-score of 85.69%, and a 98.10% area under the curve which is higher compared to other pre-trained models. A comprehensive comparative analysis is done to compare the proposed approach with other state-of-the-art methods. The proposed system is efficient, considerably accurate, and can aid as a clinical assistant to detect and grade diabetic retinopathy.

Skin lesion diagnosis in medical image analysis is still a difficult task. A frequent kind of cancer known as skin cancer affects people worldwide and can be fatal. As a result, early and accurate diagnosis is crucial for finding skin cancer patients. One of the most recent technologies for detecting skin cancer is dermatoscopy. For proper treatment of skin cancer, an early diagnosis is required. The early stages of skin lesions are identical, making manual diagnosis difficult. Therefore, creating computer-aided methods for classifying skin lesions will assist dermatologists in detecting skin lesions earlier and treating them more successfully. Current research indicates a significant potential for the classification of skin lesions using deep learning networks. However, issues including unbalanced datasets, poor contrast lesions, and the extraction of pointless or duplicate features still need to be resolved. This study aims to propose a transfer learning-based ensemble model for more accurate classification results. InceptionV3 and Xception are employed to build an ensemble model for classifying skin lesion images known as IncepX-Ensemble. A traditional data augmentation method was employed for the HAM10000 dataset to address the imbalanced data. This technique mitigates the class imbalance by incorporating data augmentation, enhancing model accuracy. At the experiment’s outset, we utilized the original imbalanced data, and subsequently, balanced data were employed for the proposed model. Training and testing accuracy was achieved at 86% on the imbalanced dataset. A balanced data augmentation dataset yielded 98% training and 98% test accuracy rates. We evaluated the output of the proposed model against outputs from various transfer learning models using both the original and balanced datasets.

Monitoring wind turbine performance is vital for ensuring wind turbines’ safe, efficient, and cost-effective operation over time. Using principal component analysis (PCA), k-means clustering for labeling, and an ensemble classifier for finding outliers, this study suggests a new way to find anomalies in wind turbines. The primary objective is to improve the precision of anomaly detection in wind turbines by leveraging machine-learning techniques. The proposed methodology utilizes the output of the PCA-Kmeans model to label supervisory control and data acquisition (SCADA) data. Furthermore, a stacking ensemble classifier is employed to refine the model’s precision. Our proposed model achieved a classification accuracy of 99%, which is a significant improvement compared to existing approaches. The significance of this study lies in its potential to enable more efficient wind turbine operation by identifying and resolving anomalies that may reduce their performance. This can ultimately contribute to achieving a sustainable and renewable energy future.

The COVID-19 pandemic has profoundly impacted human societies, resulting in the loss of millions of lives and slowing economic growth worldwide. This devastating pandemic underscores the gravity of viral threats and led to multifaceted consequences, including loss of livelihoods, dynamic labor force migration, and significant ramifications on mental health. Furthermore, different scientific institutions and companies are attempting to accelerate research and innovation by analyzing large data corpus for fighting against the pandemic. In this research study, an advanced approach based on automated Latent Dirichlet Allocation (LDA) is suggested dealing with a large data corpus for efficiently providing visualization of sentiment analysis and discovered topics. This innovative approach seeks to interrogate a substantial pandemic corpus, delving into the intricacies of public sentiment and discerning evolving trends pertinent to the pandemic. A sophisticated 10-topic LDA model was implemented, revealing Topic 8 as the most prevalent, with a frequency peak of 22.29, eclipsing other enumerated topics. We employ text-mining techniques like WordCloud and Word2Vec to offer insights into specific terms relevant to the pandemic, such as “Origin,” “Symptom,” “Diagnostic,” and “Transmission.” Applying the t-SNE method enriches the analysis by visually unraveling semantic clusters within the corpus. The subsequent phase involves modeling strategic topics within the corpus through an unsupervised LDA-based approach, leveraging our suggested framework. This novel perspective contributes to a deeper understanding of the underlying dynamics by analyzing a large data corpus quickly and automatically for providing visualization of discovered topics aiming to aid front-line workers, healthcare practitioners, and community support to fight against the pandemic.

Over the past decade, breast cancer has been the most common type of cancer in women. Different methods were proposed for breast cancer detection. These methods mainly classify and categorize malignant and Benign tumors. Machine learning is a practical approach for breast cancer classification. Data mining and classification are effective methods to predict and categorize breast cancer. The optimum classification for detecting Breast Cancer (BC) is ensemble-based. The ensemble approach involves using multiple ways to find the best possible solution. This study used the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. We created a voting ensemble classifier that combines four different machine learning models: Extra Trees Classifier (ETC), Light Gradient Boosting Machine (LightGBM), Ridge Classifier (RC), and Linear Discriminant Analysis (LDA). The proposed ELRL-E approach achieved an accuracy of 97.6%, a precision of 96.4%, a recall of 100%, and an F1 score of 98.1%. Various output evaluations are used to evaluate the performance and efficiency of the proposed model and other classifiers. Overall, the recommended strategy performed better. Results are directly compared with the individual classifier and different recognized state-of-the-art classifiers. The primary objective of this study is to identify the most influential ensemble machine learning classifier for breast cancer detection and diagnosis in terms of accuracy and AUC score.

Understanding road conditions is essential for implementing effective road safety measures and driving solutions. Road situations encompass the day-to-day conditions of roads, including the presence of vehicles and pedestrians. Surveillance cameras strategically placed along streets have been instrumental in monitoring road situations and providing valuable information on pedestrians, moving vehicles, and objects within road environments. However, these video data and information are stored in large volumes, making analysis tedious and time-consuming. Deep learning models are increasingly utilized to monitor vehicles and identify and evaluate road and driving comfort situations. However, the current neural network model requires the recognition of situations using time-series video data. In this paper, we introduced a multi-directional detection model for road situations to uphold high accuracy. Deep learning methods often integrate long short-term memory (LSTM) into long-term recurrent network architectures. This approach effectively combines recurrent neural networks to capture temporal dependencies and convolutional neural networks (CNNs) to extract features from extensive video data. In our proposed method, we form a multi-directional long-term recurrent convolutional network approach with two groups equipped with CNN and two layers of LSTM. Additionally, we compare road situation recognition using convolutional neural networks, long short-term networks, and long-term recurrent convolutional networks. The paper presents a method for detecting and recognizing multi-directional road contexts using a modified LRCN. After balancing the dataset through data augmentation, the number of video files increased, resulting in our model achieving 91% accuracy, a significant improvement from the original dataset.

Global warming, a significant outcome of climate change, exerts detrimental effects on the daily lives of individuals and industries. As a result, there is an increased demand for Electric Vehicles (EVs) to reduce carbon emissions contributing to climate change. This shift underscores the critical need for accurate estimation of the State of Charge (SoC) in battery systems, which is essential for optimizing EVs' performance and ensuring effective energy utilization. This paper introduces a methodically constructed and tested SoC prediction model utilizing a comprehensive dataset derived from various driving cycles and battery records. The battery performance of EVs was assessed in our study. The essence of our innovation resides in the meticulous choice of representative driving cycles, effectively replicating real-world conditions. This methodology improves the model's capacity to apply to various driving patterns and conditions. During these cycles, a comprehensive set of battery data, encompassing voltage, current, temperature, and SoC, was systematically documented to facilitate thorough analysis. To achieve superior accuracy and robustness, our predictive model considers the strengths of the Extra Tree Regressor (ETR) and Light Gradient Boosting algorithms. Our experimental results demonstrate the remarkable performance of the ETR model in predicting SoC, surpassing the LightGBM model. The ETR model exhibited higher R2 values of 0.9983 and lower Root Mean Square Error (RMSE) of 0.62, Mean Absolute Error (MAE) of 0.085, and Mean Squared Error (MSE) of 0.39 values, underscoring its superiority. The research emphasizes the considerable significance of battery capacity in effectively predicting the SoC of EVs. Our research highlights the significant importance of battery capacity in accurately forecasting the SoC of EVs. The proposed model facilitates accurate SoC predictions, improving energy management in EVs to optimize battery utilization and support informed decisions toward sustainable mobility.

Despite the increasing attention on smart farms as a solution to rural issues such as aging agricultural populations, a shortage of young farmers, decreased production area, and reduced investment leading to stagnant income, exports, and growth rates, many farms still rely on traditional methods like cultivating tangerines in open fields. Despite this, increasing farm income requires producing high-quality tangerines and selling them at premium prices, with fruit sweetness being a crucial factor. Therefore, there is a need to examine the close correlation between tangerine quality and sweetness. In this paper, we use deep learning with the PyCaret library to predict and analyze tangerine sweetness using data from seven regions in Jeju and 13 comprehensive factors influencing sweetness, including terrain, temperature, humidity, precipitation, sunlight, wind speed, acidity, sugar-acid ratio, and others. Although applying all 13 factors could achieve over 90% accuracy, our study, limited to seven factors, still achieves a respectable 82.4% prediction accuracy, demonstrating the significant impact of weather data on sweetness. Moreover, these optimistic predictions enable the estimation of tangerine quality and price formation in the market for the coming year, allowing tangerine farmers and related agencies to respond to market conditions proactively. Furthermore, by applying these data to smart farms to control factors influencing tangerine sweetness, it is anticipated that high-quality tangerine production and increased farm income can be achieved.

Introduction: Distributed power supply has increasingly taken over as the energy industry’s primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources.Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master–slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster.Results: Simulation results show that the proposed framework has better performance under different operating conditions.Discussion: The energy storage operators and numerous energy storage users can implement master–slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win–win conditions.

In this research, the design and use of combined systems for the simultaneous production of water, heat, and energy have been proposed, and, to fulfill the water, electricity, and heat demands of a hotel, modeling of the multi-effect evaporative desalination (MED) and combined heat and power (CHP) generation system was done. Then, the design of these two systems was administered in a combined way. This design was applied in order to evaluate the economy of the combined system compared to separate systems. The performed scenario was executed every 24 h during the two seasons of the year. The genetic algorithm was used to optimize this system, and it was considered the objective function to minimize the annual costs. The results showed that the nominal capacity of the gas turbine and backup boiler in the CHP + MED + thermal energy storage (TES) system was (14%) larger and (8.2%) smaller, respectively, compared to the CHP+ MED system. In addition, by using the energy storage tank in the combined CHP + MED system, 5.1% of the annual costs were reduced.

Since lithium-ion batteries (LIBs) are essential to many different sectors, accurate estimates of their Remaining Useful Life (RUL) are necessary to maximize Battery Management Systems (BMS). In this study, we introduce an innovative approach that combines machine learning techniques to create a hybrid model, enhancing the precision and reliability of battery analysis. Our proposed model leverages the power of k-Nearest Neighbors (kNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) algorithms to capture complex relationships and patterns in battery data effectively. Our major objective is to precisely estimate the residual energy and RUL of LIBs, allowing for the efficient evaluation of battery health and deterioration over time. We meticulously curate a comprehensive dataset comprising essential battery parameters, including capacity, voltage, cycle, and temperature. The proposed hybrid model achieves impressive results with an R2 value of 0.996457, a minimal RMSE of 0.016861, and a low MAE of 0.008956. Our analysis provides valuable insights for optimizing battery performance, informed maintenance planning, and enhancing energy storage system efficiency.

Predictive Maintenance (PdM) of lithium-ion batteries has garnered significant attention in recent years due to their widespread application as energy supplies in various industrial equipment, including automated guided vehicles and battery Electric Vehicles (EVs). Accurately estimating these batteries’ Remaining Useful Life (RUL) is crucial for ensuring optimal performance, preempting unexpected failures, and minimizing maintenance costs. This article focuses on the importance of RUL prediction for lithium-ion batteries and its implications in predictive maintenance. We suggest a novel method based on machine learning techniques using optimization parameters to accurately predict the RUL of lithium-ion batteries. Our approach uses several battery performance variables, such as voltage, current, and temperature, to build a prediction model to anticipate the battery’s RUL precisely. We compare the performance of our suggested process with existing models for battery RUL prediction, incorporating Harris Hawks Optimization (HHO) for hyperparameter tuning. We evaluate the performance of our approach on a dataset of lithium-ion batteries and compare it with other related methods. On a dataset of lithium-ion batteries, we assess our method’s effectiveness and contrast it with other relevant techniques. The proposed method achieves high accuracy in predicting RUL, as evidenced by low values of metrics such as MAE, MSE, RMSE, MAPE, $R^{2}$ , and NMRSE. Also, it achieves high $R^{2}$ scores of 0.979 and 0.971 for the training and testing data, suggesting the model’s high effectiveness in predicting the RUL of batteries.

Acute lymphoblastic leukemia (ALL) is a type of leukemia cancer that arises due to the excessive growth of immature white blood cells (WBCs) in the bone marrow. The ALL rate for children and adults is nearly 80% and 40%, respectively. It affects the production of immature cells, leading to an abnormality of neurological cells and potential fatality. Therefore, a timely and accurate cancer diagnosis is important for effective treatment to improve survival rates. Since the image of acute lymphoblastic leukemia cells (cancer cells) under the microscope is complicated to recognize the difference between ALL cancer cells and normal cells. In order to reduce the severity of this disease, it is necessary to classify immature cells at an early stage. In recent years, different classification models have been introduced based on machine learning (ML) and deep learning (DL) algorithms, but they need to be improved to avoid issues related to poor generalization and slow convergence. This work enhances the diagnosis of ALL with a computer-aided system that yields accurate results by using DL techniques. This research study proposes a lightweight DL-assisted robust model based on EfficientNet-B3 using depthwise separable convolutions for classifying acute lymphoblastic leukemia and normal cells in the white blood cell images dataset. The proposed lightweight EfficientNet-B3 uses less trainable parameters to enhance the performance and efficiency of the leukemia classification. Furthermore, two publicly available datasets are considered to evaluate the effectiveness and generalization of the proposed lightweight EfficientNet-B3. In addition, different measures are employed, such as accuracy, precision, recall, and f1-score, to evaluate the effectiveness of the proposed and baseline classifiers. In addition, a detailed analysis is given to evaluate and compare the performance and efficiency of the proposed with existing pre-trained and ensemble DL classifiers. Experimental results show that the proposed model for image classification achieves better performance and outperforms the existing benchmark DL and other ensemble classifiers. Moreover, our finding suggests that the proposed lightweight EfficientNet-B3 model is reliable and generalized to facilitate clinical research and practitioners for leukemia detection.

Terahertz (THz) radiation, which has applications in the imaging of objects, non-destructive testing, satellite communication, medical diagnostics, and biosensing, has generated a great deal of attention due to its remarkable properties. This paper proposes a novel broadband filter for THz applications. The main idea is to overcome the insertion loss and bandwidth issues by modeling a frequency-domain finite difference method and guided-mode resonance (GMR). The optimal design scheme of the wideband pass filter based on the circular resonant ring is discussed by comparing the transmission parameters under various parameters. This scheme overcomes the restriction of the narrow passband bandwidth of the prior THz filters and achieves approximately 3 dB bandwidth of 0.54 THz. The proposed THz filter paper also has the advantages of a straightforward structure, low processing costs, and ease of conformal with other structures, and it can be used for stealth fighters, new communication technology, and precise instruments. In addition, when compared to existing models, the suggested filter offers higher 3 dB BW operation, increased transmittance, low insertion loss, and stable performance at various oblique angles.

Predicting the remaining useful life (RUL) is a pivotal step in ensuring the reliability of lithium-ion batteries (LIBs). In order to enhance the precision and stability of battery RUL prediction, this study introduces an innovative hybrid deep learning model that seamlessly integrates convolutional neural network (CNN) and gated recurrent unit (GRU) architectures. Our primary goal is to significantly improve the accuracy of RUL predictions for LIBs. Our model excels in its predictive capabilities by skillfully extracting intricate features from a diverse array of data sources, including voltage (V), current (I), temperature (T), and capacity. Within this novel architectural design, parallel CNN layers are meticulously crafted to process each input feature individually. This approach enables the extraction of highly pertinent information from multi-channel charging profiles. We subjected our model to rigorous evaluations across three distinct scenarios to validate its effectiveness. When compared to LSTM, GRU, and CNN-LSTM models, our CNN-GRU model showcases a remarkable reduction in root mean square error, mean square error, mean absolute error, and mean absolute percentage error. These results affirm the superior predictive capabilities of our CNN-GRU model, which effectively harnesses the strengths of both CNNs and GRU networks to achieve superior prediction accuracy. This study draws upon NASA data to underscore the outstanding predictive performance of the CNN-GRU model in estimating the RUL of LIBs.

Electric Vehicles (EVs) reliance on batteries, which currently have lower energy and power densities than liquid fuels and are prone to aging and performance degradation over time, restricts their mainstream adoption. With applications like electric vehicles and grid-scale energy storage, effective management of lithium-ion batteries is a vital enabler for a low-carbon future. Monitoring the battery’s condition of health and charge over the lifetime of an EV is, therefore, a highly pertinent issue. Battery Management Systems (BMS) are used during the operation of EVs to monitor, estimate and control battery states to ensure that batteries can function effectively and safely. Additionally, the materials composition, system design, and operating circumstances substantially impact a battery’s usable life, making it more challenging to govern and maintain battery systems. This work proposes the structure of a battery digital twin-based battery for the electronic vehicle, which has the potential to enhance BMS situational awareness greatly and enable the optimal functioning of battery storage units. Digitalization and Artificial Intelligence (AI) present an opportunity and offer. In this paper, a Digital Twin (DT) is proposed as a solution to the difficulty of onboard computation for the incremental State Of Health (SOH) and State Of Charge (SOC) by using Extreme Gradient Boost (XGBoost) model and Extended Kalman Filter (EKF) to predict the state estimate for the EV battery. The battery’s condition has been determined by using the EKF, which can provide vital information for maintenance. First, the battery’s usable life can be extended with an accurate estimate of the SOC to continue; then a learning-based prediction approach to gauge the battery’s health state is suggested in order to increase battery life. A SOC model is frequently retrained to depict the effects of aging, and a SOH model is often performed to foretell the reduction in the highest battery capacity. According to a result, DT models are useful for managing batteries, and full life cycle statistics are important for planning the battery’s upgrade path.

Cryptocurrency is one of the famous financial state in all over the world which cause several type of risks that effect on the intrinsic assessment of risk auditors. From the beginning the growth of cryptocurrency gives the financial business with the wide risk in term of presentation of money laundering. In the institution of financial supports such as anti-money laundering, banks and secrecy of banks proceed as a specialist of risk, manager of bank and officer of compliance which has a provocation for the related transaction through cryptocurrency and the users who hide the illegal funds.In this study, the Hierarchical Risk Parity and unsupervised machine learning applied on the cryptocurrency framework. The process of professional accounting in term of inherent risk connected with cryptocurrency regarding the occurrence likelihood and statement of financial impact. Determining cryptocurrency risks comprehended to have a high rate of occurrence likelihood and the access of private key which is unauthorized. The professional cryptocurrency experience in transaction cause the lower risk comparing the less experienced one. The Hierarchical Risk Parity gives the better output in term of returning the adjusted risk tail to get the better risk management result.The result section shows the proposed model is robust to various intervals which are re-balanced and the co-variance window estimation.

With the rapid development of online advertising, click fraud is a serious issue for the internet market. Click fraud is a dishonest attempt to improve a website’s profit or deplete an advertiser’s budget by clicking on pay-per-click advertisements. For an extended period, this illegal act has a threat to the industrial sectors. As a result, these businesses hesitate to advertise their items on mobile apps and websites, as numerous groups attempt to take advantage of themes. To safely advertise their services and products online, a robust mechanism is needed for efficient click fraud detection. To tackle this issue, an ensemble architecture of machine learning and deep learning is proposed to detect click fraud in online advertisement campaigns. The proposed ensemble architecture consists of a Convolutional Neural Network (CNN), and a Bidirectional Long Short-Term Memory network (BiLSTM) is used to extract hidden features, while the Random Forest (RF) is used for classification. The main objective of the proposed research study is to develop a hybrid DL model for automatic feature extraction from clicks data and then process through an RF classifier into two classes, such as fraudulent and non-fraudulent clicks. Furthermore, a preprocessing module is developed to preprocess data by dealing with categorical attributes and imbalanced data to enhance the reliability and consistency of the clicks data. In addition, different evaluation criteria are used to evaluate and compare the performance of the proposed CNN-BiLSTM-RF with the ensemble and standalone models. The experimental results indicate that our ensemble architecture achieved the accuracy of 99.19 ± 0.08%, precision 99.89 ± 0.03%, sensitivity 98.50 ± 0.11%, F1-score 99.19 ± 0.08% and specificity 99.89 ± 0.03%. Furthermore, our proposed architecture produced superior results compared to other developed ensemble and conventional models. Moreover, our proposed ensemble architecture can be used as a safeguard against click fraud for pay-per-click advertising to facilitate industries for the safe and reliable promotion of their products.

Information sharing is one of the huge topics in social media platform regarding the daily news related to events or disasters happens in nature or its human-made. The automatic urgent need identification and sharing posts and information delivery with a short response are essential tasks in this area. The key goal of this research is developing a solution for management of disasters and emergency response using social media platforms as a core component. This process focuses on text analysis techniques to improve the process of authorities in terms of emergency response and filter the information using the automatically gathered information to support the relief efforts. Specifically, we used state-of-art Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) based on supervised and unsupervised learning using social media datasets to extract real-time content related to the emergency events to comfort the fast response in a critical situation. Similarly, the blockchain framework used in this process for trust verification of the detected events and eliminating the single authority on the system. The main reason of using the integrated system is to improve the system security and transparency to avoid sharing the wrong information related to an event in social media.

With the advent of information technology, the amount of online data generation has been massive. Recommendation systems have become an effective tool in filtering information and solving the problem of information overload. Machine learning algorithms to build these recommendation systems require well-balanced data in terms of class distribution, but real-world datasets are mostly imbalanced in nature. Imbalanced data imposes a classifier to focus more on the majority class, neglecting other classes of interests and thus hindering the predictive performance of any classification model. There exist many traditional techniques for oversampling minority classes. Still, generative adversarial networks (GAN) have been showing excellent results in generating realistic synthetic tabular data that keeps the probability distribution of the original data intact. In this paper, we propose a hybrid GAN approach to solve the data imbalance problem to enhance recommendation systems’ performance. We implemented conditional Wasserstein GAN with gradient penalty to generate tabular data containing both numerical and categorical values. We also augmented auxiliary classifier loss to enforce the model to explicitly generate data belonging to the minority class. We designed the discriminator architecture with the concept of PacGAN to receive m-packed samples as input instead of a single input. This inclusion of the PacGAN architecture eliminated the mode collapse problem in our proposed model. We did a two-fold evaluation of our model. Firstly based on the quality of the generated data and secondly on how different recommendation models perform using the generated data compared to original data.

Pneumothorax, a life-threatening disease, needs to be diagnosed immediately and efficiently. The prognosis, in this case, is not only time-consuming but also prone to human errors. So, an automatic way of accurate diagnosis using chest X-rays is the utmost requirement. To date, most of the available medical image datasets have a class-imbalance (CI) issue. The main theme of this study is to solve this problem along with proposing an automated way of detecting pneumothorax. To find the optimal approach for CI problem, we first compare the existing approaches and find that under-bagging method (referred as data-level-ensemble formed by creating subsets of majority class and then combining each subset with all samples of minority class) outperforms other existing approaches. After selection of best approach for CI problem, we propose a novel framework, named as VDV model, for pneumothorax detection from highly imbalance dataset. The proposed VDV model is a complex model-level ensemble of data-level-ensembles and uses three convolutional neural networks (CNN) including VGG16, VGG-19, and DenseNet-121 as fixed feature extractors. In each data-level-ensemble, features extracted from one of the pre-defined CNN architectures are fed to support vector machine (SVM) classifier, and output is calculated using the voting method. Once outputs from the three data-level-ensembles (corresponding to three different CNN architectures as feature extractor) are obtained, then, again, the voting method is used to calculate the final prediction. Our proposed framework is tested on the SIIM ACR Pneumothorax dataset and Random Sample of NIH Chest X-ray dataset (RS-NIH). For the first dataset, 85.17% Recall with 86.0% Area under the Receiver Operating Characteristic curve (AUC) is attained. For the second dataset, 90.9% Recall with 95.0% AUC is achieved with a random split of data while 85.45% recall with 77.06% AUC is obtained with a patient-wise split of data. The comparison of our results for both the datasets with related work proves the effectiveness of proposed VDV model for pneumothorax detection.

Recycling used plastic bottles is a significant step towards environmental protection and land pollution. Lifestyle changes in developing countries such as South Korea have substantially impacted the increase in the use rate of plastic waste year by year. Plastic bottles of various types usually have varied recycling values. Human labor is used to categorize and handle recyclable waste in most countries manually. This study aims to provide an automated recyclable transparent plastic bottle classification system that can be used to replace existing trash disposal methods. Studies on the usefulness of Transfer Learning (TL) and Ensemble Learning (EL) techniques in image categorization have been conducted recently. At first developed InceptonV3, Xception, ResNet152, and DenseNet169 based TL structure. Then to enhance the level, we have proposed an ensemble model with InceptionV3 and Xception, named IncepX-Ensemble, to classify images in well-manner and poorly transparent plastic bottle images. After that, to evaluate the proposed algorithm, we have applied data augmentation to overcome the imbalanced problem. In our research, the accuracy for predicting transparent plastic bottles value reached 99.76% accuracy. The proposed ensemble model’s potential use and limitations have also has examined. This method provides the image classification of transparent plastic bottles and has essential potential value for environmental protection and pollution control.

The rapid development of information and communication technology has fostered a natural integration of technology and design. As a result, there is increasing interest in Augmented Reality (AR) business card systems that leverage digital media. This research aims to advance the design of an AR-based participatory business card information system in line with contemporary trends. Key aspects of this study include applying technology to acquire contextual information from paper business cards, transmitting it to a server, and delivering it to mobile devices; facilitating interactivity between users and content through a screen interface; providing multimedia business content (video, image, text, 3D elements) via image markers recognized by users on mobile devices, while also adapting the type and method of content delivery. The AR business card system designed in this research enhances traditional paper business cards by incorporating visual information and interactive elements and automatically generating buttons linked to phone numbers, location information, and homepages. This innovative approach enables users to interact and enriches their overall experience while adhering to strict quality control measures.

Social media network is one of the important parts of human life based on the recent technologies and developments in terms of computer science area. This environment has become a famous platform for sharing information and news on any topics and daily reports, which is the main era for collecting data and data transmission. There are various advantages of this environment, but in another point of view there are lots of fake news and information that mislead the reader and user for the information needed. Lack of trust-able information and real news of social media information is one of the huge problems of this system. To overcome this problem, we have proposed an integrated system for various aspects of blockchain and natural language processing (NLP) to apply machine learning techniques to detect fake news and better predict fake user accounts and posts. The Reinforcement Learning technique is applied for this process. To improve this platform in terms of security, the decentralized blockchain framework applied, which provides the outline of digital contents authority proof. More specifically, the concept of this system is developing a secure platform to predict and identify fake news in social media networks.

During recent developments, cryptocurrency has become a famous key factor in financial and business opportunities. However, the cryptocurrency investment is not visible regarding the market’s inconsistent aspect and volatility of high prices. Due to the real-time prediction of prices, the previous approaches in price prediction doesn’t contain enough information and solution for forecasting the price changes. Based on the mentioned problems in cryptocurrency price prediction, we proposed a machine learning-based approach to price prediction for a financial institution. The proposed system contains the blockchain framework for secure transaction environment and Reinforcement Learning algorithm for analysis and prediction of price. The main focus of this system is on Litecoin and Monero cryptocurrencies. The results show the presented system accurate the performance of price prediction higher than another state-of-art algorithm.

Many camera apps and online video conference solutions support instant selfie segmentation or virtual background function for entertainment, aesthetic, privacy, and security reasons. A good number of studies show that Deep-Learning based segmentation model (DSM) is a reasonable choice for selfie segmentation, and the ensemble of multiple DSMs can improve the precision of the segmentation result. However, it is not fit well when we apply these approaches directly to the image segmentation in a video. This paper proposes an N-Frames (NF) ensemble approach for a selfie segmentation in a video using an ensemble of multiple DSMs to achieve a high-performance automatic segmentation. Unlike the N-Models (NM) ensemble which executes multiple DSMs at once for every single video frame, the proposed NF ensemble executes only one DSM upon a current video frame and combines segmentation results of previous frames to produce the final result. For the experiment, we use four state-of-the-art image segmentation models to make an ensemble. We evaluated the proposed approach using 81 videos dataset with a single-person view collected from publicly available websites. To measure the performance of segmentation models, Intersection over Union (IoU), IoU standard deviation, false prediction rate, Memory Efficiency Rate and Computing power Efficiency Rate parameters were considered. The average IoU values of the Two-Models NM ensemble, Two-Frames NF ensemble, Three-Models NM ensemble and Three-Frames NF ensemble were 95.1868%, 95.1253%, 95.3667% and 95.1734% each, whereas the average IoU value of single models was 92.9653%. The result shows that the proposed NF ensemble approach improves the accuracy of selfie segmentation by more than 2% on average. The result of cost efficiency measurement shows that the proposed method consumes less computing power like single models.

Renewable energy sources produce electricity without causing increment in pollution, and solar energy is one of the primary renewable sources. Switching to renewable electricity is particularly impactful for companies whose emissions from purchased energy are the primary source. The Renewable Energy (RE100) initiative provides awareness to governments and the general public. Therefore, organizations must now move from renewable energy sources to clean energy sources. Solar panels are the primary source of renewable energy. However, a harsh environment or solar panel malfunction can lead to missing data, which causes various problems, such as data processing complexity, severe biases, and commitment to data quality. Optimal orientation and tilt angle for solar panels effectively get more energy from the solar panels. We have used machine learning to predict the optimal angle for a solar panel according to the season and time. This article studies solar panel data’s photovoltaic energy generation value and proposes a machine learning model based on the stacking ensemble learning technique. Three ML models, including catboost, XGboost, and random forest, are ensebmled. Experimental data are obtained by setting up sixteen solar panels with different combinations of tilt and direction. The performance of the proposed method is compared with other ML and statistical models. We obtained a regression score (R2) of 0.86 and a mean absolute percentage error (MAPE) of 2.54%.

Smart grids are developing rapidly, leading to the need for accurate forecasts of power consumption. However, developing a precise time series model for energy forecasting is difficult. It has to be trained using optimal meteorological features such as temperature and time lags to qualify for a beneficial model. We have proposed an approach that uses an ensemble machine learning model based on XGBoost, support vector regressor (SVR), and K-nearest neighbors (KNN) regressor algorithms. We have also used the genetic algorithm (GA) to predict total load consumption from optimal feature selection. Using Jeju island's electricity consumption data as a case study shows that the proposed ensemble model optimized with GA is more accurate than the individual machine learning models. Using only the best-selected weather and time features, the proposed model records all the features of a complicated time series and shows a reduction in the mean absolute percentage error (MAPE) and the root mean square log error for the week ahead forecasts. We got 3.35 % MAPE of the three months test data by applying the proposed model. The smart grids operators can manage resources effectively to provide excellent services to the consumers based on the recommended model outcomes.

Three-dimensional image modeling is essential in many scientific disciplines, including computer vision and precision agriculture. So far, various methods of creating three-dimensional (3D) models have been considered. However, the processing of transformation matrices of each input image data is not controlled. Site-specific crop mapping is essential because it helps farmers determine yield, biodiversity, energy, crop coverage, etc. Clifford Geometric Algebraic understanding of signaling and image processing has become increasingly important in recent years. Geometric Algebraic treats multi-dimensional signals in a holistic way to maintain relationship between side sizes and prevent loss of information. This article has used agricultural images acquired by unmanned aerial vehicles (UAVs) to construct three-dimensional models using Clifford geometric algebra. The qualitative and quantitative performance evaluation results show that Clifford geometric algebra can generate a three-dimensional geometric statistical model directly from drones' RGB images. Through peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual comparison, the proposed algorithm's performance is compared with latest algorithms. Experimental results show that proposed algorithm is better than other leading 3D modeling algorithms.

Generative adversarial networks (GAN) have been widely used in the field of image-to-image translation. In this paper, we have proposed a novel object extraction and background recovery (OEBR-GAN) model, which can extract objects from an image and then complete the image by inpainting the background of the image. This model has been developed for a solar panel installation project, where the user would like to input an original colored image of the roof, and as output, the user requires an edge detected roof image. However, the condition in user requirement is that any object that is hiding the roof edges should be removed first and the background of that part of the roof image should be recovered so that the user can obtain a complete connected edge detected image of the roof. Therefore, the model also completes the image by connecting the hidden edges of the roof. We could achieve the user objective by building a GAN model with a dual generator and dual discriminator network. The generators have been built using an encoder-decoder network with and without skip connections and the discriminators have been built using deep convolutional neural networks and encoder architecture. Quantitative comparisons in the result section shows that OEBR-GAN performs much better than other adversarial models on our collected dataset.

Demand for electric kickboards is increasing specifically in tourist-centric regions worldwide. In order to gain a competitive edge and to provide quality service to customers, it is essential to properly deploy rental electric kickboards (e-kickboards) at the time and place customers want. However, it is necessary to study how to divide the region to predict electric mobility demand by region. Therefore, this study is made to more accurately predict future demand based on past regional customers’ electric mobility demand data. We have proposed a novel electric kickboard demand prediction in spatiotemporal dimension using clustering-aided bagging regressor. We have used electric kickboard usage data from a Jeju, South Korea-based company. As a result of the experiment, it was found that the accuracy before using clustering-based bagging regressor and when the region was divided by the clustering method, the performance was improved, and we have achieved a regression score R2 of 93.42 using our proposed approach. We have compared our proposed approach with other state-of-the-art models, and we have also compared our model with different other combinations of bagging regressors. This study can be helpful for companies to meet the user’s demand for a better quality of service.

In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local service provider’s access to information and help them manage their short-term supply more effectively. This study developed the forecasting model using real-time data and weather information from Jeju Island, South Korea. Cluster analysis under the rental pattern of the electric kickboard is a component of the forecasting processes. We cannot achieve noticeable results at first because of the low amount of training data. We require a lot of data to produce a solid prediction result. For the sake of the subsequent experimental procedure, we created synthetic time-series data using a generative adversarial networks (GAN) approach and combined the synthetic data with the original data. The outcomes have shown how the GAN-based synthetic data generation approach has the potential to enhance prediction accuracy. We employ an ensemble model to improve prediction results that cannot be achieved using a single regressor model. It is a weighted combination of several base regression models to one meta-regressor. To anticipate the daily demand in this study, we create an ensemble model by merging three separate base machine learning algorithms, namely CatBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The effectiveness of the suggested strategies was assessed using some evaluation indicators. The forecasting outcomes demonstrate that mixing synthetic data with original data improves the robustness of daily demand forecasting and outperforms other models by generating more agreeable values for suggested assessment measures. The outcomes further show that applying ensemble techniques can reasonably increase the forecasting model’s accuracy for daily electric kickboard demand.

Pregnancy and early childhood are two vulnerable times when immunological plasticity is at its peak and exposure to stress may substantially raise health risks. However, to separate the effects of adversity during vulnerable times of the lifetime from those across the entire lifespan, we require deeper phenotyping. Stress is one of the challenges which everyone can face with this issue. It is a type of feeling which contains mental pressure and comes from daily life matters. There are many research and investments regarding this problem to overcome or control this complication. Pregnancy is a susceptible period for the child and the mother taking stress can affect the child’s health after birth. The following matter can happen based on natural disasters, war, death or separation of parents, etc. Early Life Stress (ELS) has a connection with psychological development and metabolic and cardiovascular diseases. In the following research, the main focus is on Early Life Stress control during pregnancy of a healthy group of women that are at risk of future disease during their pregnancy. This study looked at the relationship between retrospective recollections of childhood or pregnancy hardship and inflammatory imbalance in a group of 53 low-income, ethnically diverse women who were seeking family-based trauma treatment after experiencing interpersonal violence. Machine learning Convolutional Neural Networks (CNNs) are applied for stress detection using short-term physiological signals in terms of non-linear and for a short term. The focus concepts are heart rate, and hand and foot galvanic skin response.

The instability and variable lifetime are the benefits of high efficiency and low-cost issues in lithium-ion batteries.An accurate equipment’s remaining useful life prediction is essential for successful requirement-based maintenance to improve dependability and lower total maintenance costs. However, it is challenging to assess a battery’s working capacity, and specific prediction methods are unable to represent the uncertainty. A scientific evaluation and prediction of a lithium-ion battery’s state of health (SOH), mainly its remaining useful life (RUL), is crucial to ensuring the battery’s safety and dependability over its entire life cycle and preventing as many catastrophic accidents as feasible. Many strategies have been developed to determine the prediction of the RUL and SOH of lithium-ion batteries, including particle filters (PFs). This paper develops a novel PF-based technique for lithium-ion battery RUL estimation, combining a Kalman filter (KF) with a PF to analyze battery operating data. The PF method is used as the core, and extreme gradient boosting (XGBoost) is used as the observation RUL battery prediction. Due to the powerful nonlinear fitting capabilities, XGBoost is used to map the connection between the retrieved features and the RUL. The life cycle testing aims to gather precise and trustworthy data for RUL prediction. RUL prediction results demonstrate the improved accuracy of our suggested strategy compared to that of other methods. The experiment findings show that the suggested technique can increase the accuracy of RUL prediction when applied to a lithium-ion battery’s cycle life data set. The results demonstrate the benefit of the presented method in achieving a more accurate remaining useful life prediction.

Cryptocurrency, often known as virtual or digital currency, is a safe platform and a key component of the blockchain that has recently attracted much interest. Utilizing blockchain technology, bitcoin transactions are recorded in blocks that provide detailed information on all financial transactions. Artificial intelligence (AI) has significant applicability in several industries because of the abundance and processing capacity of large data. One of the main issues is the absence of explanations for AI algorithms in the current decision-making standards. For instance, there is no deep-learning-based reasoning or control for the system’s input or output processes. More particularly, the bias for adversarial attacks on the process interface and learning characterizes existing AI systems. This study suggests an AI-based trustworthy architecture that uses decentralized blockchain characteristics such as smart contracts and trust oracles. The decentralized consensuses of AI predictors are also decided by this system using AI, enabling secure cryptocurrency transactions, and utilizing the blockchain technology and transactional network analysis. By utilizing AI for a thorough examination of a network, this system’s primary objective is to improve the performance of the bitcoin network in terms of transactions and security. In comparison to other state-of-the-art systems, the results demonstrate that the proposed system can achieve very accurate output.

This study uses the API of Upbit, one of Korea’s cryptocurrency exchanges, to predict continuous time series for a limited period and cryptocurrencies using LSTM, a machine learning technique. The trading (buying and selling) point algorithm presented in this study was used to conduct experimental research on efficient profit creation for cryptocurrency investment. Several related studies have shown the results of time series prediction for long-term forecasts, such as a week or several months. Still, they have not attempted to make an ultra-short-term prediction in units of one minute. This paper attempts such a 1 min prediction. This is an experiment to create efficient profits by setting efficient trading (buying and selling) points using machine learning techniques and repeating these operations by an algorithm. Applying it to cryptocurrency shows the possibility of time series prediction.

Rapid advancements in the medical field have drawn much attention to automatic emotion classification from EEG data. People’s emotional states are crucial factors in how they behave and interact physiologically. The diagnosis of patients’ mental disorders is one potential medical use. When feeling well, people work and communicate more effectively. Negative emotions can be detrimental to both physical and mental health. Many earlier studies that investigated the use of the electroencephalogram (EEG) for emotion classification have focused on collecting data from the whole brain because of the rapidly developing science of machine learning. However, researchers cannot understand how various emotional states and EEG traits are related. This work seeks to classify EEG signals’ positive, negative, and neutral emotional states by using a stacking-ensemble-based classification model that boosts accuracy to increase the efficacy of emotion classification using EEG. The selected features are used to train a model that was created using a random forest, light gradient boosting machine, and gradient-boosting-based stacking ensemble classifier (RLGB-SE), where the base classifiers random forest (RF), light gradient boosting machine (LightGBM), and gradient boosting classifier (GBC) were used at level 0. The meta classifier (RF) at level 1 is trained using the results from each base classifier to acquire the final predictions. The suggested ensemble model achieves a greater classification accuracy of 99.55%. Additionally, while comparing performance indices, the suggested technique outperforms as compared with the base classifiers. Comparing the proposed stacking strategy to state-of-the-art techniques, it can be seen that the performance for emotion categorization is promising.

Alzheimer’s disease is dementia that impairs one’s thinking, behavior, and memory. It starts as a moderate condition affecting areas of the brain that make it challenging to retain recently learned information, causes mood swings, and causes confusion regarding occasions, times, and locations. The most prevalent type of dementia, called Alzheimer’s disease (AD), causes memory-related problems in patients. A precise medical diagnosis that correctly classifies AD patients results in better treatment. Currently, the most commonly used classification techniques extract features from longitudinal MRI data before creating a single classifier that performs classification. However, it is difficult to train a reliable classifier to achieve acceptable classification performance due to limited sample size and noise in longitudinal MRI data. Instead of creating a single classifier, we propose an ensemble voting method that generates multiple individual classifier predictions and then combines them to develop a more accurate and reliable classifier. The ensemble voting classifier model performs better in the Open Access Series of Imaging Studies (OASIS) dataset for older adults than existing methods in important assessment criteria such as accuracy, sensitivity, specificity, and AUC. For the binary classification of with dementia and no dementia, an accuracy of 96.4% and an AUC of 97.2% is attained.

Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines’ consistency and timely maintenance can enhance their performance and dependability. Still, the traditional routine configuration makes detecting faults of wind turbines difficult. Supervisory control and data acquisition (SCADA) produces reliable and affordable quality data for the health condition of wind turbine operations. For wind power to be sufficiently reliable, it is crucial to retrieve useful information from SCADA successfully. This article proposes a new AdaBoost, K-nearest neighbors, and logistic regression-based stacking ensemble (AKL-SE) classifier to classify the faults of the wind turbine condition monitoring system. A stacking ensemble classifier integrates different classification models to enhance the model’s accuracy. We have used three classifiers, AdaBoost, K-nearest neighbors, and logistic regression, as base models to make output. The output of these three classifiers is used as input in the logistic regression classifier’s meta-model. To improve the data validity, SCADA data are first preprocessed by cleaning and removing any abnormal data. Next, the Pearson correlation coefficient was used to choose the input variables. The Stacking Ensemble classifier was trained using these parameters. The analysis demonstrates that the suggested method successfully identifies faults in wind turbines when applied to local 3 MW wind turbines. The proposed approach shows the potential for effective wind energy use, which could encourage the use of clean energy.

Traffic congestion is a significant issue in many countries today. The suggested method is a novel control method based on multiple intersections considering the kind of traffic light and the duration of the green phase to determine the optimal balance at intersections by using fuzzy logic control, for which the balance should be adaptable to the unchanging behavior of time. It should reduce traffic volume in transport, average waits for each vehicle, and collisions between cars by controlling this balance in response to the typical behavior of time and randomness in traffic conditions. The proposed method is investigated at intersections using a sampling multi-agent system to set traffic light timings appropriately. The program is provided with many intersections, each of which is an independent entity exchanging information with the others. The stability per entity is proven separately. Simulation results show that Takagi–Sugeno (TS) fuzzy modeling performs better than Takagi–Sugeno (TS) fixed-time scheduling in decreasing the length of queueing times for vehicles.

Efforts to decarbonize the world have shown a quick increase in electric vehicles (EVs), limiting increasing pollution. During this electric transportation revolution, lithium-ion batteries (LIBs) play a vital role in storing energy. To determine the range of an electric vehicle (EV), the state of charge and the state of health (SOH) of the battery pack is essential. Access to high-quality data on battery parameters is a crucial challenge for researchers working in the energy storage domain due primarily to confidentiality constraints on manufacturers of batteries and EVs. This paper proposes a hybrid framework for predicting the state of a lithium-ion battery for electric vehicles (EV). Electric vehicles are growing worldwide because of their environmental and sustainability advantages. Batteries are replacing fossil fuels in electric vehicles. In order to prevent failure, Li-ion batteries in electric vehicles should be operated and controlled in a controlled and progressive manner to ensure increased efficiency and safety. An extreme gradient boosting (XGBoost) algorithm is used in this paper to estimate the state of health (SOH) of lithium-ion batteries used in electric vehicles. The model is subjected to error analysis to optimize the battery’s performance parameter. The model undergoes an error analysis to optimize its performance parameters. Furthermore, a state of health (SOH) estimation method based on the extreme gradient boosting algorithm with accuracy correction is proposed here to improve the accuracy of state of health (SOH) estimation for lithium-ion batteries. To describe the aging process of batteries, we extract several features such as average voltages, voltage differences, current differences, and temperature differences. The extreme gradient boosting (XGBoost) model for estimating the state of health (SOH) of lithium-ion batteries is based on the ensemble learning algorithm’s higher prediction accuracy and generalization ability. Experimental results suggest that the boundary gradient lifting algorithm model is capable of more accurate prediction.

Plastic bottle recycling has a crucial role in environmental degradation and protection. Position and background should be the same to classify plastic bottles on a conveyor belt. The manual detection of plastic bottles is time consuming and leads to human error. Hence, the automatic classification of plastic bottles using deep learning techniques can assist with the more accurate results and reduce cost. To achieve a considerably good result using the DL model, we need a large volume of data to train. We propose a GAN-based model to generate synthetic images similar to the original. To improve the image synthesis quality with less training time and decrease the chances of mode collapse, we propose a modified lightweight-GAN model, which consists of a generator and a discriminator with an auto-encoding feature to capture essential parts of the input image and to encourage the generator to produce a wide range of real data. Then a newly designed weighted average ensemble model based on two pre-trained models, inceptionV3 and xception, to classify transparent plastic bottles obtains an improved classification accuracy of 99.06%.

E-learning is a popular area in terms of learning from social media websites in various terms and contents for every group of people in this world with different knowledge backgrounds and jobs. E-learning sites help users such as students, business workers, instructors, and those searching for different educational institutions. Excluding the benefits of this system, there are various challenges that the users face in online platforms. One of the important challenges is the true information and right content based on these resources, search results and quality. This research proposes virtual and intelligent agent-based recommendation, which requires users’ profile information and preferences to recommend the proper content and search results based on their search history. We applied Natural Language Processing (NLP) techniques and semantic analysis approaches for the recommendation of course selection to e-learners and tutors. Moreover, machine learning performance analysis applied to improve the user rating results in the e-learning environment. The system automatically learns and analyzes the learner characteristics and processes the learning style through the clustering strategy. Compared with the recent state-of-the-art in this field, the proposed system and the simulation results show the minimizing number of metric errors compared to other works. The achievements of the presented approach are providing a comfortable platform to the user for course selection and recommendations. Similarly, we avoid recommending the same contents and courses. We analyze the user preferences and improving the recommendation system performance to provide highly related content based on the user profile situation. The prediction accuracy of the proposed system is 98% compared to hybrid filtering, self organization systems and ensemble modeling.

The battery management system in an electric vehicle must be reliable and durable to forecast the state of charge. Considering that battery degradation is generally nonlinear, state of charge (SOC) estimation with lower degradation can be challenging. Lithium-ion batteries are highly dependent on the knowledge of aging, which is usually costly or not available online. In this paper, we suggest the state of charge estimation of lithium-ion battery systems by using an extreme gradient boosting algorithm for electric vehicles application, which acquires the nonlinear relationship model can with offline training. The extreme gradient boosting algorithm is the tree on based learning, which effectively performs and speeds. Voltage-time data used as an input of this system from the partial constant current phase; the proposed algorithm improves the accuracy of predicting the relevant. Additionally, no initial state of charge is required in our proposed method; thus, estimating the state of charge can consider each battery state.

As society grows, the urbanized population proliferates, and urbanization accelerates. Increasing traffic problems affect the normal process of the city. The urban transportation system is vital to the effective functioning of any city. Science and technology are critical elements in improving traffic performance in urban areas. In this paper, a novel control strategy based on selecting the type of traffic light and the duration of the green phase to achieve an optimal balance at intersections is proposed. This balance should be adaptable to fixed behavior of time and randomness in a traffic situation; the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control the crashing of cars. Due to the distribution of urban traffic and the urban transportation network among intelligent methods for traffic control, the multi-factor system has been designed as a suitable, intelligent, emerging, and successful model. Intersection traffic control is checked through proper traffic light timing modeled on multi-factor systems. Its ability to solve complex real-world problems has made multiagent systems a field of distributed artificial intelligence that is rapidly gaining popularity. The proposed method was investigated explicitly at the intersection through an appropriate traffic light timing by sampling a multiagent system. It consists of many intersections, and each of them is considered an independent agent that shares information with each other. The stability of each agent is proved separately. One of the salient features of the proposed method for traffic light scheduling is that there is no limit to the number of intersections and the distance between intersections. In this paper, we proposed method model predictive control for each intersection’s stability; the simulation results show that the predictive model controller in this multi-factor model predictive system is more valuable than scheduling in the fixed-time method. It reduces the length of vehicle queues.

Every year approximately 1.24 million people are diagnosed with blood cancer. While the rate increases each year, the availability of data for each kind of blood cancer remains scarce. It is essential to produce enough data for each blood cell type obtained from bone marrow aspirate smears to diagnose rare types of cancer. Generating data would help easy and quick diagnosis, which are the most critical factors in cancer. Generative adversarial networks (GAN) are the latest emerging framework for generating synthetic images and time-series data. This paper takes microscopic cell images, preprocesses them, and uses a hybrid GAN architecture to generate synthetic images of the cell types containing fewer data. We prepared a single dataset with expert intervention by combining images from three different sources. The final dataset consists of 12 cell types and has 33,177 microscopic cell images. We use the discriminator architecture of auxiliary classifier GAN (AC-GAN) and combine it with the Wasserstein GAN with gradient penalty model (WGAN-GP). We name our model as WGAN-GP-AC. The discriminator in our proposed model works to identify real and generated images and classify every image with a cell type. We provide experimental results demonstrating that our proposed model performs better than existing individual and hybrid GAN models in generating microscopic cell images. We use the generated synthetic data with classification models, and the results prove that the classification rate increases significantly. Classification models achieved 0.95 precision and 0.96 recall value for synthetic data, which is higher than the original, augmented, or combined datasets.

The popularity of cryptocurrency in recent years has gained a lot of attention among researchers and in academic working areas. The uncontrollable and untraceable nature of cryptocurrency offers a lot of attractions to the people in this domain. The nature of the financial market is non-linear and disordered, which makes the prediction of exchange rates a challenging and difficult task. Predicting the price of cryptocurrency is based on the previous price inflations in research. Various machine learning algorithms have been applied to predict the digital coins’ exchange rate, but in this study, we present the exchange rate of cryptocurrency based on applying the machine learning XGBoost algorithm and blockchain framework for the security and transparency of the proposed system. In this system, data mining techniques are applied for qualified data analysis. The applied machine learning algorithm is XGBoost, which performs the highest prediction output, after accuracy measurement performance. The prediction process is designed by using various filters and coefficient weights. The cross-validation method was applied for the phase of training to improve the performance of the system.