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As digital technologies have advanced more rapidly, the number of paper documents recently converted into a digital format has exponentially increased. To respond to the urgent need to categorize the growing number of digitized documents, the classification of digitized documents in real time has been identified as the primary goal of our study. A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement. Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits, which were not conceivable ten years ago. Deep learning aids in comprehending input patterns so that object classes may be predicted. The segmentation process divides the input image into separate segments for a more thorough image study. This study proposes a deep learning-enabled framework for automated document classification, which can be implemented in higher education. To further this goal, a dataset was developed that includes seven categories: Diplomas, Personal documents, Journal of Accounting of higher education diplomas, Service letters, Orders, Production orders, and Student orders. Subsequently, a deep learning model based on Conv2D layers is proposed for the document classification process. In the final part of this research, the proposed model is evaluated and compared with other machine-learning techniques. The results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%, 94.79%, 94.62%, 94.43%, 94.07% in accuracy, precision, recall, F-score, and AUC-ROC, respectively. The achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker. [ABSTRACT FROM AUTHOR]

Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this review, the comparative analysis of existing literature surveys is explored. This paper aims to provide an overview of AIbased UAV swarms, different cameras and sensors, image processing, and machine learning (ML) algorithms for image analysis having the purpose of monitoring and disease identification. Brassica plants are focused as they are grown on wider scales globally. Brassica species, the commonly infected diseases, and different types of disease detectionmethods are discussed. Investigations show the significance of using UAV swarms for growthmonitoring growth for yield estimation, health monitoring, water status monitoring and irrigation management, nutrition disorders monitoring, pest and disease detection, and pesticide and fertilizer spraying in Brassica plants. Finally, some challenges of swarm-based applications are also addressed that require future consideration. The significance of this paper is that it suggests its readers embrace swarm-based technologies in the pursuit of more efficient production with relevant economic benefits. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (EfficientDP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while protecting the parameter information uploaded by the client during the training process. Specifically, in the federated learning framework, this article uses an adaptive DP-FL method for gradient descent training, which makes the model converge faster than traditional stochastic gradient descent. In addition, due to model convergence, noise should be reduced accordingly. This paper introduces an early stopping mechanism to improve data availability. This paper demonstrates the performance improvement of the Efficient DP-FL algorithm through simulation experiments on real MNIST and Fashion-MNIST datasets. Experimental showthat the efficient DP-FL algorithm is significantly superior to other algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the issue of preserving the privacy of parties involved in blockchain transactions has garnered significant attention. To ensure privacy protection for both sides of the transaction, many researchers are using ring signature technology instead of the original signature technology. However, in practice, identifying the signer of anillegal blockchain transactiononce ithas beenplacedon the chainnecessitates a signature technique that offers conditional anonymity. Some illegals can conduct illegal transactions and evade the lawusing ring signatures,which offer perfect anonymity. This paper firstly constructs a conditionally anonymous linkable ring signature using the Diffie-Hellman key exchange protocol and the Elliptic Curve Discrete Logarithm, which offers a non-interactive process for finding the signer of a ring signature in a specific case. Secondly, this paper's proposed scheme is proven correct and secure under Elliptic Curve Discrete Logarithm Assumptions. Lastly, compared to previous constructions, the scheme presented in this paper provides a non-interactive, efficient, and secure confirmation process. In addition, this paper presents the implementation of the proposed scheme on a personal computer, where the confirmation process takes only 2, 16, and 24ms for ring sizes of 4, 24 and 48, respectively, and the confirmation process can be combined with a smart contract on the blockchain with a tested millisecond level of running efficiency. In conclusion, the proposed scheme offers a solution to the challenge of identifying the signer of an illegal blockchain transaction, making it an essential contribution to the field. [ABSTRACT FROM AUTHOR]

The default scheduler of Apache Hadoop demonstrates operational inefficiencies when connecting external sources and processing transformation jobs. This paper has proposed a novel scheduler for enhancement of the performance of the Hadoop Yet Another Resource Negotiator (YARN) scheduler, called the Adaptive Node and Container Aware Scheduler (ANACRAC), that aligns cluster resources to the demands of the applications in the real world. The approach performs to leverage the user-provided configurations as a unique design to apportion nodes, or containers within the nodes, to application thresholds. Additionally, it provides the flexibility to the applications for selecting and choosing which node's resources they want to manage and adds limits to prevent threshold breaches by adding additional jobs as needed. Node or container awareness can be utilized individually or in combination to increase efficiency. On top of this, the resource availability within the node and containers can also be investigated.This paper also focuses on the elasticity of the containers and self-adaptiveness depending on the job type. The results proved that 15%-20% performance improvement was achieved compared with the node and container awareness feature of the ANACRAC. It has been validated that this ANACRAC scheduler demonstrates a 70%-90% performance improvement compared with the default Fair scheduler. Experimental results also demonstrated the success of the enhancement and a performance improvement in the range of 60% to 200% when applications were connected with external interfaces and high workloads. [ABSTRACT FROM AUTHOR]

Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learningbased methods are believed to be promising for detecting ethereum Ponzi schemes. However, there are still some flaws in current research, e.g., insufficient feature extraction of Ponzi scheme smart contracts, without considering class imbalance. In addition, there is room for improvement in detection precision. Aiming at the above problems, this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting (AdaBoost) algorithm. Firstly, this paper uses the n-gram algorithm to extract more comprehensive contract opcode features and combine them with contract account features, which helps to improve the feature extraction effect. Meanwhile, adaptive synthetic sampling (ADASYN) is introduced to deal with class imbalanced data, and integrated with the Adaboost classifier. Finally, this paper uses the improved AdaBoost classifier for the identification of Ponzi scheme contracts. Experimentally, this paper tests our model in real-world smart contracts and compares it with representative methods in the aspect of F1-score and precision. Moreover, this article compares and discusses the state of art methods with our method in four aspects: data acquisition, data preprocessing, feature extraction, and classifier design. Both experiment and discussion validate the effectiveness of our model. [ABSTRACT FROM AUTHOR]

More businesses are deploying powerful Intrusion Detection Systems (IDS) to secure their data and physical assets. Improved cyber-attack detection and prevention in these systems requires machine learning (ML) approaches. This paper examines a cyber-attack prediction system combining feature selection (FS) and ML. Our technique's foundation was based on Correlation Analysis (CA), Mutual Information (MI), and recursive feature reduction with cross-validation. To optimize the IDS performance, the security features must be carefully selected from multiple-dimensional datasets, and our hybrid FS technique must be extended to validate our methodology using the improved UNSW-NB 15 and TON_IoT datasets. Our technique identified 22 key characteristics in UNSW-NB-15 and 8 in TON_IoT. We evaluated prediction using seven ML methods: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Multilayer Perceptron (MLP) classifiers. The DT, RF, NB, and MLP classifiers helped our model surpass the competition on both datasets. Therefore, the investigational outcomes of our hybrid model may help IDSs defend business assets from various cyberattack vectors. [ABSTRACT FROM AUTHOR]

The number of blogs and other forms of opinionated online content has increased dramatically in recent years. Many fields, including academia and national security, place an emphasis on automated political article orientation detection. Political articles (especially in the Arab world) are different from other articles due to their subjectivity, in which the author's beliefs and political affiliation might have a significant influence on a political article. With categories representing the main political ideologies, this problem may be thought of as a subset of the text categorization (classification). In general, the performance of machine learning models for text classification is sensitive to hyperparameter settings. Furthermore, the feature vector used to represent a document must capture, to some extent, the complex semantics of natural language. To this end, this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting (CatBoost) method combined with a multi-level feature concept. Extracting features at multiple levels can enhance the model's ability to discriminate between different classes or patterns. Each level may capture different aspects of the input data, contributing to a more comprehensive representation. CatBoost, a robust and efficient gradient-boosting algorithm, is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles. A dataset of political Arabic texts collected from diverse sources, including postings and articles, is used to assess the suggested technique. Conservative, reform, and revolutionary are the three subcategories of these opinions. The results of this study demonstrate that compared to other frequently used machine learning models for text classification, the CatBoost method using multi-level features performs better with an accuracy of 98.14%. [ABSTRACT FROM AUTHOR]

Text mining presents unique challenges in extracting meaningful information from the vast volumes of digital documents. Traditional filter feature selection methods often fall short in handling the complexities of short text data. To address this issue, this paper presents a novel approach to feature selection in text classification, aiming to overcome challenges posed by high dimensionality and reduced accuracy in the face of increasing digital document volumes. Unlike traditional filter feature selection techniques, the proposed method, Multivariate Relevance Frequency Analysis, offers a tailored solution for diverse text data types. By integrating positive, negative, and dependency relevance computations, the proposed approach effectively prunes features, enhancing classification performance. Extensive experimental analysis has been performed for the proposed model and compared with several standard existing feature selection models on five datasets involving short and long texts using four standard classifiers. The results indicate that the proposed model has the highest macro-F1 score of 94% for the SMS dataset, 78.1% for the SLS dataset, 89.4% for the AYSC dataset, 71.32% for the Reuters dataset, and 98.63% for the 20Newsgroup dataset. The statistical analysis also indicates that the proposed model provides better performance with both short texts such as messages and reviews as well as long texts containing documents, with superior performance for short-text data. The comparative analysis shows that the proposed model offers better performance than many other standard filtration models. [ABSTRACT FROM AUTHOR]

The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm algorithm, fusing the capabilities of the Artificial Bee Colony (ABC) and Firefly algorithms, aimed at improving feature selection in gene expression classification. The proposed method undergoes rigorous validation through statistical machine learning techniques and quantitative parameter evaluation, with comprehensive comparisons to established techniques in the field. The findings underscore the superiority of the hybrid Swarm Intelligence approach for feature selection in gene expression classification, offering promising prospects for enhancing cancer diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been involved in several domains. Despite the advantages of using artificial intelligence techniques, some crucial limitations prevent them from being implemented in specific domains and locations. The accuracy, poor quality of gathered data, and processing time are considered major concerns in implementing machine learning techniques, certainly in low-end smart devices. This paper aims to introduce a novel pre-treatment technique dedicated to image text detection that uses the images' pixel divergence and similarity to reduce the image size. Mitigating the image size while keeping its features improves the model training time with an acceptable accuracy rate. The mitigation is reached by gathering similar image pixels in one pixel based on calculated values of the standard deviation σ, where we consider that two pixels are similar if they have approximately the same σ values. The work proposes a new pipeline approach that reduces the size of the image in the input and intermediate layers of a deep learning model based on merged pixels using standard deviation values instead of the whole image. The experimental results prove that this technique significantly improves the performance of existing text detection methods, particularly in challenging scenarios such as using low-end IoT devices that offer low contrast or noisy backgrounds. Compared with other techniques, the proposed technique can potentially be exploited for text detection in IoT-gathered multimedia data with reasonable accuracy in a short computation time. Evaluation of the MSRA-TD500 dataset demonstrates the remarkable performance of our approach, Standard Deviation Network (σNet), with precision and recall values of 93.8% and 85.6%, respectively, that outperform recent research results. [ABSTRACT FROM AUTHOR]

To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At last, we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals. Therefore, the application of ISMO is validated by ten benchmark functions and feature selection. It is proved that the proposed method can resolve the problem of feature selection. [ABSTRACT FROM AUTHOR]

The products of an archival culture in colleges and universities are the final result of the development of archival cultural resources, and the development of archival cultural effects in colleges and universities should be an important part of improving the artistic level of libraries. The existing RippleNet model doesn't consider the influence of key nodes on recommendation results, and the recommendation accuracy is not high. Therefore, based on the RippleNet model, this paper introduces the influence of complex network nodes into the model and puts forward the Cn RippleNet model. The performance of the model is verified by experiments, which provide a theoretical basis for the promotion and recommendation of its cultural products of universarchives, solve the problem that RippleNet doesn't consider the influence of key nodes on recommendation results, and improve the recommendation accuracy. This paper also combs the development course of archival cultural products in detail. Finally, based on the Cn-RippleNet model, the cultural effect of university archives is recommended and popularized. [ABSTRACT FROM AUTHOR]

Specific medical data has limitations in that there are not many numbers and it is not standardized. to solve these limitations, it is necessary to study how to efficiently process these limited amounts of data. In this paper, deep learning methods for automatically determining cardiovascular diseases are described, and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was conducted. The cardiac CT images include several parts of the body such as the heart, lungs, spine, and ribs. The preprocessing step proposed in this paper divided CT image data into regions of interest and other regions usingK-means clustering and the Grabcut algorithm. We compared the deep learning performance results of original data, data using only K-means clustering, and data using both K-means clustering and the Grabcut algorithm. All data used in this paper were collected at Soonchunhyang University Cheonan Hospital in Korea and the experimental test proceeded with IRB approval. The training was conducted using Resnet 50, VGG, and Inception resnet V2 models, and Resnet 50 had the best accuracy in validation and testing. Through the preprocessing process proposed in this paper, the accuracy of deep learning models was significantly improved by at least 10% and up to 40%. [ABSTRACT FROM AUTHOR]

As the scale of the power system continues to expand, the environment for power operations becomes more and more complex. Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately. Therefore, more reliable and accurate security control methods are urgently needed. In order to improve the accuracy and reliability of the operation risk management and control method, this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal hybrid convolutional neural network. To provide early warning and control of targeted risks, first, the video stream is framed adaptively according to the pixel changes in the video stream. Then, the optimized MobileNet is used to extract the feature map of the video stream, which contains both time-series and static spatial scene information. The feature maps are combined and non-linearly mapped to realize the identification of dynamic operating scenes. Finally, training samples and test samples are produced by using the whole process image of a power company in Xinjiang as a case study, and the proposed algorithm is compared with the unimproved MobileNet. The experimental results demonstrated that the method proposed in this paper can accurately identify the type and start and end time of each operation link in the whole process of electric power operation, and has good real-time performance. The average accuracy of the algorithm can reach 87.8%, and the frame rate is 61 frames/s, which is of great significance for improving the reliability and accuracy of security control methods. [ABSTRACT FROM AUTHOR]

In the objective world, how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine learning. Fuzzy support vector machine (FSVM) not only deals with the classification problems for training samples with fuzzy information, but also assigns a fuzzy membership degree to each training sample, allowing different training samples to contribute differently in predicting an optimal hyperplane to separate two classes with maximum margin, reducing the effect of outliers and noise, Quantum computing has super parallel computing capabilities and holds the promise of faster algorithmic processing of data. However, FSVM and quantum computing are incapable of dealing with the complexity and uncertainty of big data in an efficient and accurate manner. This paper research and propose an efficient and accurate quantum fuzzy support vector machine (QFSVM) algorithm based on the fact that quantum computing can efficiently process large amounts of data and FSVM is easy to deal with the complexity and uncertainty problems. The central idea of the proposed algorithm is to use the quantum algorithm for solving linear systems of equations (HHL algorithm) and the least-squares method to solve the quadratic programming problem in the FSVM. The proposed algorithm can determine whether a sample belongs to the positive or negative class while also achieving a good generalization performance. Furthermore, this paper applies QFSVM to handwritten character recognition and demonstrates that QFSVM can be run on quantum computers, and achieve accurate classification of handwritten characters. When compared to FSVM, QFSVM's computational complexity decreases exponentially with the number of training samples. [ABSTRACT FROM AUTHOR]

During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse, when patient emotion is fear, cry or sad. The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm. The proposed Convolution Neural Networks (CNN) architecture with DnCNN preprocessing enhances the performance of recognition. The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras. The proposed method accuracy is more when compared to other methods. It detects the chances of suicide attempt based on stress level and emotional recognition. [ABSTRACT FROM AUTHOR]

With the increasing number of connected devices in the Internet of Things (IoT) era, the number of intrusions is also increasing. An intrusion detection system (IDS) is a secondary intelligent system for monitoring, detecting and alerting against malicious activity. IDS is important in developing advanced security models. This study reviews the importance of various techniques, tools, andmethods used in IoT detection and/or prevention systems. Specifically, it focuses on machine learning (ML) and deep learning (DL) techniques for IDS. This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles. To speed up the detection of recent attacks, the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network (CNN), which performs better than a support vector machine (SVM). Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy. The nearest class mean classifier is applied during the testing phase to identify new attacks. Experimental results using the AWID dataset, which is one of the most common open intrusion detection datasets, revealed a higher detection accuracy (94%) compared to SVM and random forest methods. [ABSTRACT FROM AUTHOR]

Software Defined Network (SDN) and Network Function Virtualization (NFV) technology promote several benefits to network operators, including reduced maintenance costs, increased network operational performance, simplified network lifecycle, and policies management. Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration (NFV MANO), and malicious attacks in different scenarios disrupt the NFV Orchestrator (NFVO) and Virtualized InfrastructureManager (VIM) lifecycle management related to network services or individual Virtualized Network Function (VNF). This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users. An anomaly detector investigates these identified risks and provides secure network services. It enables virtual network security functions and identifies anomalies in Kubernetes (a cloud-based platform). For training and testing purpose of the proposed approach, an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf, Neptune, Teardrop, Pod, Land, IPsweep, etc., categorized as Probing (Prob), Denial of Service (DoS), User to Root (U2R), and Remote to User (R2L) attacks. An anomaly detector is anticipated with the capabilities of a Machine Learning (ML) technique, making use of supervised learning techniques like Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Extreme Gradient Boosting (XGBoost). The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes. RF classifier has shown better outcomes (99.90% accuracy) than other classifiers in detecting anomalies/intrusions in the containerized environment. [ABSTRACT FROM AUTHOR]

Recent years have witnessed significant advancements in the field of character recognition, thanks to the revolutionary introduction of machine learning techniques. Among various types of character recognition, offline Handwritten Character Recognition (HCR) is comparatively more challenging as it lacks temporal information, such as stroke count and direction, ink pressure, and unexpected handwriting variability. These issues contribute to a poor level of precision,which calls for the adoption of anomaly detection techniques to enhanceOptical Character Recognition (OCR) schemes. Previous studies have not researched unsupervised anomaly detection usingMLP for handwriting recognition. Therefore, this study proposes a novel approach for enhanced English letter identification based on a Multi-Layer Perceptron (MLP) neural network and an anomaly detection method. The MLP neural network is trained on a massive dataset of letters, enabling it to achieve high accuracy in recognizing letters from different font styles. Additionally, to further enhance the performance of the system, an anomaly detectionmethod is employed to identify and eliminate any outliers or anomalies in the recognition process. The combination of these two techniques results in a highly accurate and robust letter recognition system that can be applied to a wide range of practical applications. The hybrid approach aims to increase the accuracy of English letter identification while minimizing misclassification, i.e., classification issues associated with recognizing English letters in images. To identify handwritten letters based on their underlying patterns, discriminant samples with extracted attributes were fed into a neural network,whichwas then used to categorize the data. This paper achieved significant improvements in English letter identification by coupling the anomaly detection algorithm and the multilayer perceptron neural network.When tested on the UCI-English letters dataset, this hybrid technique boosted the accuracy from 84.11% using themultiple neural networks to an impressive 92.84% with the proposedmethod.Moreover, the experimental results of the study have demonstrated that the hybrid method outperforms modern classification techniques for handwriting letter recognition. Overall, the proposed approach has tremendous potential to advance the field of character recognition, especially for offline handwritten character recognition, and can pave the way for developing more accurate and efficient OCR systems in the future. [ABSTRACT FROM AUTHOR]

Energy management systems for residential and commercial buildingsmust use an appropriate and efficientmodel to predict energy consumption accurately. To deal with the challenges in powermanagement, the short-termPower Consumption (PC) prediction for household appliances plays a vital role in improving domestic and commercial energy efficiency. Big data applications and analytics have shown that data-driven load forecasting approaches can forecast PC in commercial and residential sectors and recognize patterns of electric usage in complex conditions. However, traditional Machine Learning (ML) algorithms and their features engineering procedure emphasize the practice of inefficient and ineffective techniques resulting in poor generalization. Additionally, different appliances in a home behave contrarily under distinct circumstances, making PC forecasting more challenging. To address these challenges, in this paper a hybrid architecture using an unsupervised learning strategy is investigated. The architecture integrates a one-dimensional Convolutional Neural Network (CNN) based Autoencoder (AE) and online sequential Extreme Learning Machine (ELM) for commercial and residential short-term PC forecasting. First, the load data of different buildings are collected and cleaned from various abnormalities. A subsequent step involves AE for learning a compressed representation of spatial features and sending them to the online sequential ELM to learn nonlinear relations and forecast the final load. Finally, the proposed network is demonstrated to achieve State-of-the-Art (SOTA) error metrics based on two benchmark PC datasets for residential and commercial buildings. The Mean Square Error (MSE) values obtained by the proposed method are 0.0147 and 0.0121 for residential and commercial buildings datasets, respectively. The obtained results prove that our model is suitable for the PC prediction of different types of buildings. [ABSTRACT FROM AUTHOR]

The recent global pandemic has resulted in growth in themedical and healthcare sectors. Applications used in these domains have become more advanced and digitally integrated. Sensor-based Internet of Things (IoT) devices are increasing in healthcare andmedical units. The emerging trend with the use of IoT devices in medical healthcare is termed as Internet of Medical Things (IoMT). The instruments used in these healthcare units comprise various sensors that can record patient body observations. These recorded observations are streamed across Internetbased channels to be stored and analyzed in centralized servers. Patient diagnostics are performed based on the information retrieved from these devices. Machine learning and artificial intelligence play a significant role in diagnostic mechanisms and identifying diseases across observation sets. The data collected by these observation sets are analyzed closely with the help of Artificial Neural Networks (ANN). This distributed system of devices and servers raises privacy concerns. Blockchain is a technology that can preserve the privacy of the information collected from devices. A blockchain is a mechanism in which information goes across various network nodes. A centralized architecture where information flows from the sensor devices to a centralized server risks large-scale information leak. This paper proposes a prototype and a model for collecting information and protecting the data from intrusion. Any application that resides in the cloud and collects data from a sensor based IoT device can be prone to intrusion. The detection occurs at an early stage and in a fraction of the time. The model uses artificial neural networks and gets evaluated across various datasets. Improved accuracy and performance with data analyzed at the IoT-based devices prove the efficiency of the model proposed in this study. The stability and accuracy of the results after testing these applications make the model sustainable and acceptable across healthcare systems. [ABSTRACT FROM AUTHOR]

Android devices are popularly available in the commercial market at different price levels for various levels of customers. The Android stack is more vulnerable compared to other platforms because of its open-source nature. There are many android malware detection techniques available to exploit the source code and find associated components during execution time. To obtain a better result we create a hybrid technique merging static and dynamic processes. In this paper, in the first part, we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Multicollinearity problem is one of the drawbacks in the existing system. In the proposed work, a novel PCA (Principal Component Analysis) based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach. The Android Sensitive Permission is one major key point to be considered while detecting malware. We select vulnerable columns based on features like sensitive permissions, application program interface calls, services requested through the kernel, and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign. The final goal of this paper is to check benchmarking datasets collected from various repositories like virus share, Github, and the Canadian Institute of cyber security, compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate. [ABSTRACT FROM AUTHOR]

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics. The framework models the user behavior as sequences of events representing the user activities at such a network. The represented sequences are then fitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users. Thus, the model can recognize frequencies of regular behavior to profile the user manner in the network. The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regular or irregular behavior. The importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the network. Typically detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network, including users. Therefore, the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reflect a normal network workflow. In contrast, the irregular patterns can trigger an alert for a potential cyber-attack. The framework has been fully described where the evaluation metrics have also been introduced. The experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM 1. The paper has been concluded with providing the potential directions for future improvements. [ABSTRACT FROM AUTHOR]

Sleep apnea syndrome (SAS) is a breathing disorder while a person is asleep. The traditional method for examining SAS is Polysomnography (PSG). The standard procedure of PSG requires complete overnight observation in a laboratory. PSG typically provides accurate results, but it is expensive and time consuming. However, for people with Sleep apnea (SA), available beds and laboratories are limited. Resultantly, it may produce inaccurate diagnosis. Thus, this paper proposes the Internet of Medical Things (IoMT) framework with a machine learning concept of fully connected neural network (FCNN) with k-nearest neighbor (k-NN) classifier. This paper describes smart monitoring of a patient's sleeping habit and diagnosis of SA using FCNN-KNN+ average square error (ASE). For diagnosing SA, the Oxygen saturation (SpO2) sensor device is popularly used for monitoring the heart rate and blood oxygen level. This diagnosis information is securely stored in the IoMT fog computing network. Doctors can carefully monitor the SA patient remotely on the basis of sensor values, which are efficiently stored in the fog computing network. The proposed technique takes less than 0.2 s with an accuracy of 95%, which is higher than existing models. [ABSTRACT FROM AUTHOR]

One of the fast-growing disease affecting women's health seriously is breast cancer. It is highly essential to identify and detect breast cancer in the earlier stage. This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately. Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images. Existing methods focused on using various conventional and machine learning methods for processing natural and medical images. It is inadequate for the image where the coarse structure matters most. Most of the input images are downscaled, where it is impossible to fetch all the hidden details to reach accuracy in classification. Whereas deep learning algorithms are high efficiency, fully automatic, have more learning capability using more hidden layers, fetch as much as possible hidden information from the input images, and provide an accurate prediction. Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images. The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms. [ABSTRACT FROM AUTHOR]

Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments. Therefore, in this paper, a weed image analysis approach has been proposed for the system of weed classification. In this system, for preprocessing, a Homomorphic filter is exploited to diminish the environmental factors. While, for feature extraction, an adaptive feature extraction method is proposed that exploited edge detection. The proposed technique estimates the directions of the edges while accounting for non-maximum suppression. This method has several benefits, including its ease of use and ability to extend to other types of features. Typically, low-level details in the formof features are extracted to identify weeds, and additional techniques for detecting cultured weeds are utilized if necessary. In the processing of weed images, certain edges may be verified as a footstep function, and our technique may outperform other operators such as gradient operators. The relevant details are extracted to generate a feature vector that is further given to a classifier for weed identification. Finally, the features have been used in logistic regression for weed classification. The model was assessed against logistic regression that accurately identified different kinds of weed images in naturalistic domains. The proposed approach attained weighted average recognition of 98.5% against the weed images dataset. Hence, it is assumed that the proposed approach might help in the weed classification system to accurately identify narrow and broad weeds taken captured in real environments. [ABSTRACT FROM AUTHOR]

Attribute reduction, also known as feature selection, for decision information systems is one of the most pivotal issues in machine learning and data mining. Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problem of attribute reduction.Unfortunately, the intuitionistic fuzzy sets based methods have not received much interest, while these methods are well-known as a very powerful approach to noisy decision tables, i.e., data tables with the low initial classification accuracy. Therefore, this paper provides a novel incremental attribute reductionmethod to dealmore effectivelywith noisy decision tables, especially for highdimensional ones. In particular, we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions. It should be noted that the intuitionistic fuzzypartitiondistance iswell-knownas aneffectivemeasure todetermine important attributes.More interestingly, an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects. This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables. Besides, some experiments are conducted on real datasets to show that ourmethod is far superior to the fuzzy rough set basedmethods in terms of the size of reduct and the classification accuracy. [ABSTRACT FROM AUTHOR]

Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption compared to state-of-the-art deep learning algorithms. Our proposed system maintains two steps. At first, this work extracted the local features of an image by using a local binary pattern descriptor, and then we used deep learning to extract global features. The proposed approach has achieved excellent accuracy and high performance. The performance of the proposed method was tested on three benchmark datasets: the realworld masked faces dataset (RMFD), the simulated masked faces dataset (SMFD), and labeled faces in the wild (LFW). Performancemetrics for the proposed technique weremeasured in terms of accuracy, precision, recall, and F1-score. Results indicated the efficiency of the proposed technique, providing accuracies of 99.86%, 99.98%, and 100% for RMFD, SMFD, and LFW, respectively. Moreover, the proposed method outperformed state-of-the-art deep learningmethods in the recent bibliography for the same problem under study and on the same evaluation datasets. [ABSTRACT FROM AUTHOR]

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems. One of the challenges in software security testing is test case prioritization, which aims to reduce redundancy in fault occurrences when executing test suites. By effectively applying test case prioritization, both the time and cost required for developing secure software can be reduced. This paper proposes a test case prioritization technique based on the Ant Colony Optimization (ACO) algorithm, a metaheuristic approach. The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection (APFD) metric, comparing it with traditional techniques. It has been applied to a Mobile PaymentWallet application to validate the proposed approach. The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric. The ACO-based technique achieves an APFD of approximately 76%, two percent higher than the second-best optimal ordering technique. These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases, saving time and improving software security overall. [ABSTRACT FROM AUTHOR]

Audio copyright is a crucial issue in the music industry, as it protects the rights and interests of creators and distributors. This paper studies the current situation of digital music copyright certification and proposes a music copyright certification framework based on "blockchain + edge computing mode," abbreviated as MBE, by integrating edge computing into the Hyperledger Fabric system. MBE framework compresses and splits the audio into small chunks, performs Fast Fourier Transform (FFT) to extract the peak points of each frequency and combines them to obtain unique audio fingerprint information. After being confirmed by various nodes on the Fabric alliance chain, audio fingerprint information and copyright owner information are recorded on the chain and broadcast to all participants. Blockchain technology's characteristics of being tamper-proof and traceable not only reform the trust mechanism of copyright protection but also endow edge computing with the ability to resist tampering and single-point attack, greatly enhancing the robustness of the music copyright certification system. Meanwhile, edge computing mode improves Fabric blockchain's processing speed and transaction throughput. Experimental results show that MBE's performance is better than traditional systems regarding efficiency, storage demand and security. Compared to the traditional Fabric system without edge computingmode,MBE exhibits a 53% higher deposition efficiency and a 48% lower storage space requirement. [ABSTRACT FROM AUTHOR]

Computational intelligence (CI) is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems. The CI is useful in the UAV domain as it produces efficient, precise, and rapid solutions. Besides, unmanned aerial vehicles (UAV) developed a hot research topic in the smart city environment. Despite the benefits of UAVs, security remains a major challenging issue. In addition, deep learning (DL) enabled image classification is useful for several applications such as land cover classification, smart buildings, etc. This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification (MDLS-UAVIC) model in a smart city environment. The major purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels. The proposedMDLS-UAVIC model follows a two-stage process: encryption and image classification. The encryption technique for image encryption effectively encrypts the UAV images.Next, the image classification process involves anXception-based deep convolutional neural network for the feature extraction process. Finally, shuffled shepherd optimization (SSO) with a recurrent neural network (RNN) model is applied for UAV image classification, showing the novelty of the work. The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset, and the outcomes are examined in various measures. It achieved a high accuracy of 98%. [ABSTRACT FROM AUTHOR]

Data mining plays a crucial role in extracting meaningful knowledge fromlarge-scale data repositories, such as data warehouses and databases. Association rule mining, a fundamental process in data mining, involves discovering correlations, patterns, and causal structures within datasets. In the healthcare domain, association rules offer valuable opportunities for building knowledge bases, enabling intelligent diagnoses, and extracting invaluable information rapidly. This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System (MLARMC-HDMS). The MLARMC-HDMS technique integrates classification and association rule mining (ARM) processes. Initially, the chimp optimization algorithm-based feature selection (COAFS) technique is employed within MLARMC-HDMS to select relevant attributes. Inspired by the foraging behavior of chimpanzees, the COA algorithm mimics their search strategy for food. Subsequently, the classification process utilizes stochastic gradient descent with a multilayer perceptron (SGD-MLP) model, while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques. This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set. We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers. Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods, achieving higher accuracy and precision rates in medical data classification tasks. The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features, thereby enhancing the diagnosis and treatment of various diseases. To provide further validation, we conduct detailed experiments on a benchmark medical dataset, revealing the superiority of the MLARMCHDMS model over other methods, with a maximum accuracy of 99.75%. Therefore, this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis. The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning. [ABSTRACT FROM AUTHOR]

Human activity recognition is a recent area of research for researchers. Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety, monitor indoor and outdoor activities, develop Tele immersion systems, or detect abnormal activity recognition. Three dimensions (3D) skeleton data is robust and somehow view-invariant. Due to this, it is one of the popular choices for human action recognition. This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence. Further proposed two neural networks: convolutional neural network recurrent neural network_1 (CNN_RNN_1), used to find the optimal features and convolutional neural network recurrent neural network network_2 (CNN_RNN_2), used to classify actions. The deep neural network-based model proposed CNN_RNN_1 and CNN_RNN_2 that uses a convolutional neural network (CNN), Long short-term memory (LSTM) and Bidirectional Long shorttermmemory (BiLSTM) layered. The system efficiently achieves the desired accuracy over state-of-the-artmodels, i.e., 88.89%. The performance of the proposed model compared with the existing state-of-the-art models. The NTURGB + D dataset uses for analyzing experimental results. It is one of the large benchmark datasets for human activity recognition. Moreover, the comparison results show that the proposed model outperformed the state-ofthe- art models. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) of the Arabic language becomes important despite scarce annotated corpora and confined sources. Arabic affect Analysis has become an active research zone nowadays. But still, the Arabic language lags behind adequate language sources for enabling the SA tasks. Thus, Arabic still faces challenges in natural language processing (NLP) tasks because of its structure complexities, history, and distinct cultures. It has gained lesser effort than the other languages. This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis (MVODRL-AA) on Arabic Corpus. The presentedMVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus. Firstly, the MVODRL-AA model follows data pre-processing and word embedding. Next, an n-gram model is utilized to generate word embeddings. A deep Q-learning network (DQLN) model is then exploited to identify and classify the effect on the Arabic corpus. At last, theMVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model, showing the novelty of the work. A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model. The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%. [ABSTRACT FROM AUTHOR]

Over the past few years, rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems. As a result, greater intelligence is necessary to effectively manage, optimize, andmaintain these systems. Due to their distributed nature, machine learning models are challenging to deploy in traditional networks. However, Software-Defined Networking (SDN) presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control planes. SDN provides a centralized network view and allows for dynamic updates of flow rules and softwarebased traffic analysis. While the programmable nature of SDN makes it easier to deploy machine learning techniques, the centralized control logic also makes it vulnerable to cyberattacks. To address these issues, recent research has focused on developing powerful machine-learning methods for detecting and mitigating attacks in SDN environments. This paper highlighted the countermeasures for cyberattacks on SDNand howcurrentmachine learningbased solutions can overcome these emerging issues. We also discuss the pros and cons of using machine learning algorithms for detecting and mitigating these attacks. Finally, we highlighted research issues, gaps, and challenges in developing machine learning-based solutions to secure the SDN controller, to help the research and network community to developmore robust and reliable solutions. [ABSTRACT FROM AUTHOR]

Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence (AI) algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy. Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson's. Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients. The healthcare industry faces two significant challenges: security and privacy issues and the personalization of cloud-trained AI models. This paper proposes a Deep Neural Network (DNN) based approach embedded in a federated learning framework to detect and diagnose brain disorders. We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients, each with different data. To lessen the over-fitting aspect, every client reviewed the outcomes in three rounds. The proposed model identifies brain disorders without jeopardizing privacy and security. The results reveal that the global model achieves an accuracy of 82.82% for detecting brain disorders while preserving privacy. [ABSTRACT FROM AUTHOR]

Kidney infection is a severe medical issue affecting individuals worldwide and increasing mortality rates. Chronic Kidney Disease (CKD) is treatable during its initial phases but can become irreversible and cause renal failure. Among the various diseases, the most prevalent kidney conditions affecting kidney function are cyst growth, kidney tumors, and nephrolithiasis. The significant challenge for the medical community is the immediate diagnosis and treatment of kidney disease. Kidney failure could result from kidney disorders like tumors, stones, and cysts if not often identified and addressed. Computer-assisted diagnostics are necessary to support clinicians' and specialists' medical assessments due to the rising prevalence of chronic renal illness, the lack of experts, and the rising rates of assessment and monitoring, mainly in developing nations. Artificial Intelligence (AI) approaches such as machine, and deep learning has been used in literature for kidney disease detection; however, they still lack performance. This paper implements a deep learning-based Convolutional Neural Network (CNN) model for the classification and prognosis of kidney disease. We use a benchmark Computed Tomography (CT) kidney dataset for experimentation. The data is pre-processed, and then CNN extracts the features from the images. Results reveal that the proposed approach accurately classifies kidney disease with a considerable accuracy of 0.992%, 0.994% precision, 0.982% recall, and 0.987% F1-score. This study suggests using the proposed fine-tuned CNN model for kidney disease detection. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has gained popularity for the containment of COVID-19 pandemic applications. Several AI techniques provide efficient mechanisms for handling pandemic situations. AI methods, protocols, data sets, and various validation mechanisms empower the users towards proper decision-making and procedures to handle the situation. Despite so many tools, there still exist conditions in which AI must go a long way. To increase the adaptability and potential of these techniques, a combination of AI and Bigdata is currently gaining popularity. This paper surveys and analyzes the methods within the various computational paradigms used by different researchers and national governments, such as China and South Korea, to fight against this pandemic. The process of vaccine development requires multiple medical experiments. This process requires analyzing datasets from different parts of the world. Deep learning and the Internet of Things (IoT) revolutionized the field of disease diagnosis and disease prediction. The accurate observations from different datasets across the world empowered the process of drug development and drug repurposing. To overcome the issues generated by the pandemic, using such sophisticated computing paradigms such as AI, Machine Learning (ML), deep learning, Robotics and Bigdata is essential. [ABSTRACT FROM AUTHOR]

With the commercialization of 5th-generation mobile communications (5G) networks, a large-scale internet of things (IoT) environment is being built. Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service (DDoS) attacks across vast IoT devices. Recently, research on automated intrusion detection using machine learning (ML) for 5G environments has been actively conducted. However, 5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data. If this data is used to train an ML model, it will likely suffer from generalization errors due to not training enough different features on the attack data. Therefore, this paper aims to study a training method to mitigate the generalization error problem of the ML model that classifies IoT DDoS attacks even under conditions of insufficient and imbalanced 5G traffic. We built a 5G testbed to construct a 5G dataset for training to solve the problem of insufficient data. To solve the imbalance problem, synthetic minority oversampling technique (SMOTE) and generative adversarial network (GAN)-based conditional tabular GAN (CTGAN) of data augmentation were used. The performance of the trained ML models was compared and meaningfully analyzed regarding the generalization error problem. The experimental results showed that CTGAN decreased the accuracy and f1-score compared to the Baseline. Still, regarding the generalization error, the difference between the validation and test results was reduced by at least 1.7 and up to 22.88 times, indicating an improvement in the problem. This result suggests that the ML model training method that utilizes CTGANs to augment attack data for training data in the 5G environment mitigates the generalization error problem. [ABSTRACT FROM AUTHOR]

Cardiotocography (CTG) represents the fetus's health inside the womb during labor. However, assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician. Digital signals from fetal monitors acquire parameters (i.e., fetal heart rate, contractions, acceleration). Objective: This paper aims to classify the CTG readings containing imbalanced healthy, suspected, and pathological fetus readings. Method: We perform two sets of experiments. Firstly, we employ five classifiers: Random Forest (RF), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) without over-sampling to classify CTG readings into three categories: healthy, suspected, and pathological. Secondly, we employ an ensemble of the above-described classifiers with the oversamplingmethod. We use a random over-sampling technique to balance CTG records to train the ensemble models. We use 3602 CTG readings to train the ensemble classifiers and 1201 records to evaluate them. The outcomes of these classifiers are then fed into the soft voting classifier to obtain the most accurate results. Results: Each classifier evaluates accuracy, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values. Results reveal that the XGBoost, LGBM, and CatBoost classifiers yielded 99% accuracy. Conclusion: Using ensemble classifiers over a balanced CTG dataset improves the detection accuracy compared to the previous studies and our first experiment. A soft voting classifier then eliminates the weakness of one individual classifier to yield superior performance of the overall model. [ABSTRACT FROM AUTHOR]

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide. This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions. The infusion of vasoactive drugs to patients endures various issues, such as variation of sensitivity and noise, which require effective and powerful systems to ensure robustness and good performance. The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy (IT2F) logic and teaching-learning-based optimization (TLBO) algorithm. This networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs successfully. To avoid the manual selection of control parameter values, the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F controller. The simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances. A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy controllers.Moreover, the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients' sensitivity to infused vasoactive drugs in a noisy environment. [ABSTRACT FROM AUTHOR]

Cancer disease is a deadliest disease cause more dangerous one. By identifying the disease through Artificial intelligence to getting the mage features directly from patients. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification. This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy. To resolve this problem, to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor. The general technique of the created approach includes four stages, such as pre-processing, segmentation, highlight extraction, and the order. From the outset, the Computerized Tomography (CT) image of the lung is taken care of to the division. When the division is done, the highlights are extricated through morphological factors for feature observation. By getting the features are analysed and the characterization is done dependent on the Deep Belief Network (DBN) which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm (CSCA) which distinguish the lung tumour, giving two classes in particular, knob or non-knob. The proposed system produce high performance as well compared to the other system. The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity, precision, affectability, and the explicitness. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused trouble in people's daily lives and ruined several economies around the world, killing millions of people thus far. It is essential to screen the affected patients in a timely and cost-effective manner in order to fight this disease. This paper presents the prediction of COVID-19 with Chest X-Ray images, and the implementation of an image processing system operated using deep learning and neural networks. In this paper, a Deep Learning, Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used for building and training neural nets. Scikit-learn was used for machine learning from end to end. Various deep learning features are used, such as Conv2D, Dense Net, Dropout, Maxpooling2D for creating the model. The proposed approach had a classification accuracy of 96.43 percent and a validation accuracy of 98.33 percent after training and testing the X-Ray pictures. Finally, a web application has been developed for general users, which will detect chest x-ray images either as covid or normal. A GUI application for the Covid prediction framework was run. A chest X-ray image can be browsed and fed into the program by medical personnel or the general public. [ABSTRACT FROM AUTHOR]

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body. It produces cancerous blood cells that disturb the human's immune system and significantly affect bone marrow's production ability to effectively create different types of blood cells like red blood cells (RBCs) and white blood cells (WBC), and platelets. Leukemia can be diagnosed manually by taking a complete blood count test of the patient's blood, from which medical professionals can investigate the signs of leukemia cells. Furthermore, two other methods, microscopic inspection of blood smears and bone marrow aspiration, are also utilized while examining the patient for leukemia. However, all these methods are labor-intensive, slow, inaccurate, and require a lot of human experience and dedication. Different authors have proposed automated detection systems for leukemia diagnosis to overcome these limitations. They have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast cells. However, these systems are more efficient, reliable, and fast than previous manual diagnosing methods. However, more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class similarity. In this paper, we have proposed a robust automated system to diagnose leukemia and its sub-types. We have classified ALL into its sub-types based on FAB classification, i.e., L1, L2, and L3 types with better performance. We have achieved 96.06% accuracy for subtypes classification, which is better when compared with the state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

Question-answering (QA) models find answers to a given question. The necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data sets. In this paper, we deal with the QA pair matching approach in QA models, which finds the most relevant question and its recommended answer for a given question. Existing studies for the approach performed on the entire dataset or datasets within a category that the question writer manually specifies. In contrast, we aim to automatically find the category to which the question belongs by employing the text classification model and to find the answer corresponding to the question within the category. Due to the text classification model, we can effectively reduce the search space for finding the answers to a given question. Therefore, the proposed model improves the accuracy of the QA matching model and significantly reduces the model inference time. Furthermore, to improve the performance of finding similar sentences in each category, we present an ensemble embedding model for sentences, improving the performance compared to the individual embedding models. Using real-world QA data sets, we evaluate the performance of the proposed QA matching model. As a result, the accuracy of our final ensemble embedding model based on the text classification model is 81.18%, which outperforms the existing models by 9.81%~14.16% point. Moreover, in terms of the model inference speed, our model is faster than the existing models by 2.61~5.07 times due to the effective reduction of search spaces by the text classification model. [ABSTRACT FROM AUTHOR]

An intrusion detection system (IDS) becomes an important tool for ensuring security in the network. In recent times, machine learning (ML) and deep learning (DL) models can be applied for the identification of intrusions over the network effectively. To resolve the security issues, this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique, called BBOFS-DRL for intrusion detection. The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network. To attain this, the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm (BOA) to elect feature subsets. Besides, DRL model is employed for the proper identification and classification of intrusions that exist in the network. Furthermore, beetle antenna search (BAS) technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency. For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model, a wide-ranging experimental analysis is performed against benchmark dataset. The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches. [ABSTRACT FROM AUTHOR]

Recently, computer assisted diagnosis (CAD) model creation has become more dependent on medical picture categorization. It is often used to identify several conditions, including brain disorders, diabetic retinopathy, and skin cancer. Most traditional CAD methods relied on textures, colours, and forms. Because many models are issue-oriented, they need a more substantial capacity to generalize and cannot capture high-level problem domain notions. Recent deep learning (DL) models have been published, providing a practical way to develop models specifically for classifying input medical pictures. This paper offers an intelligent beetle antenna search (IBAS-DTL) method for classifying medical images facilitated by deep transfer learning. The IBAS-DTL model aims to recognize and classify medical pictures into various groups. In order to segment medical pictures, the current IBASDTLM model first develops an entropy based weighting and first-order cumulative moment (EWFCM) approach. Additionally, the DenseNet-121 techniquewas used as a module for extracting features.ABASwith an extreme learning machine (ELM) model is used to classify the medical photos. A wide variety of tests were carried out using a benchmark medical imaging dataset to demonstrate the IBAS-DTL model's noteworthy performance. The results gained indicated the IBAS-DTL model's superiority over its pre-existing techniques. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) is a nondestructive real-time monitoring technology, which has been proven to be a valid way of monitoring dynamic damage to materials. The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning. Considering that the huge success of deep learning technologies, where the Recurrent Neural Network (RNN) has been widely applied to sequential classification tasks and Convolutional Neural Network (CNN) has been widely applied to image recognition tasks. A novel three-streams neural network (TSANN) model is proposed in this paper to deal with fault detection tasks. Based on residual connection and attention mechanism, each stream of the model is able to learn the most informative representation from Mel Frequency Cepstrum Coefficient (MFCC), Tempogram, and short-time Fourier transform (STFT) spectral respectively. Experimental results show that, in comparison with traditional classification methods and single-stream CNN networks, TSANN achieves the best overall performance and the classification error rate is reduced by up to 50%, which demonstrates the availability of the model proposed. [ABSTRACT FROM AUTHOR]

Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based on multi-relationship embedding. The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder. The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network (GCN) to obtain the final node embedding matrix. This strategy allows capturing of rich structural and attributes information in multi-relational networks. Experiments were conducted on different datasets with baselines, and the results show that the proposed algorithm obtains significant performance improvement in community discovery, node clustering, and similarity search tasks, and compared to the baseline with the best performance, the proposed algorithm achieves an average improvement of 3.1% on Macro-F1 and 4.7% on Micro-F1, which proves the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]