Heart disease stands as a principal cause of death worldwide, and its early prediction is essential for effective patient management and the reduction of healthcare expenditures.In this context, machine learning (ML) has emerged as a transformative tool in the healthcare sector, demonstrating a profound capability to discern intricate data patterns and furnish accurate prognostic assessments.The application of ML in cardiology is instrumental for risk prediction, early detection, and the customization of treatment protocols.The current study systematically reviews the spectrum of ML approaches applied to the prediction of heart disease, spanning supervised, unsupervised, reinforcement, and transfer learning methodologies.Data from prominent repositories such as Kaggle and the UCI Machine Learning Repository were employed to evaluate the performance of various ML algorithms, with key metrics including accuracy, sensitivity, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC).Influential predictors, namely age, gender, cholesterol levels, blood pressure, and lifestyle factors, were integral to the development of these predictive models.Particular attention was given to the exploration of ensemble methods and deep learning frameworks, which have shown to augment prediction accuracy beyond that of traditional models.This research delineates essential risk factors associated with heart disease and underscores the significance of predictive analytics in the healthcare landscape.With a focus on heterogeneous datasets and analytical techniques, the review aims to inform public health strategies and contribute to the alleviation of healthcare burdens.The elucidated findings highlight the promise of ML, particularly through the utilization of ensemble and deep learning methods, in the precursory prediction of heart disease.Such advancements enable healthcare professionals to make more informed decisions, adopt preventative interventions, and mitigate the overall impact on healthcare systems.This exhaustive review also synthesizes the efficacy and practicality of various ML algorithms, providing a valuable compendium for future research initiatives and promoting the integration of cutting-edge technologies in the management of cardiac health.
The study aimed to classify two classes of vehicles, Tuktuk and Motorcycle, using a modified RCNN model.The MAjN_IRAQ Dataset, created from a camera system in Baghdad city, was used for training, detection, and classification of some vehicles to allow them to enter some crowded streets of Baghdad and to prevent others from entering the same streets.New layers were added and the number and size of filters were changed, which led to improve the process of training, detection and classification of vehicles with high accuracy, which leads to improving the proposed model's performance.The results showed that the modified RCNN model performed better when trained for 80 epochs.It improved performance measures such as precision, recall, and F1 score measure.The model was compared to other transfer learning methods (Alex Net, VGG16, and VGG19) and showed superior results for the Tuktuk class.The training and testing time for the proposed RCNNmodified model was also lower compared to the other models.At 80 epochs, the precision for the Tuktuk class was approximately 0.94, while for the Motorcycle class, it was approximately 0.89.The TPR was higher for the Tuktuk class at approximately 0.93, while the lower value was approximately 0.84 for VGG16.When the VGG16 model was used, the F1 score was better in the Motorcycle category (about 0.95) but worse in the Tuktuk category (0.86%).Both the suggested RCNN-modified model and the Alex Net model worked well in a reasonable amount of time.
Maintenance, storage and warehousing are complex processes required in many industries such as automotive, aerospace, manufacturing and logistic companies.These processes, often, involve moving objects in crowded environments using robots or human operators.Particularly, replacement and assembly of machine parts in crowded environments when performed by a human being require high technical skills.These tasks may be performed using robots to reduce costs due to human errors and execution time.However, robots under open world assumptions could neither operate in all environments nor perform tasks not modeled by the designer.In this paper, we introduce a mixed reality system to assist human operators in moving objects in crowded environments for maintenance tasks such as: parts assembly or replacement, and storage of objects.The introduced system consists of a mobile application exploited through a hands-free VR box.The proposed Mixed Reality for Industrial Maintenance (MRIM) system enhances the perception of a human operator by overlaying 3D real world visual information and virtual objects, such as: orientation guidelines including rotating angles, moving direction and displacement of carried objects.These guidelines allow for gaining execution time, and reducing human errors that might cause industrial parts damage.The proposed work brings two main contributions.First, it makes use of a new algorithm based on recasting, named R star (R*) that allows for optimizing pathfinding in 3D space.This later outperforms the two commonly used baseline 3D pathfinding algorithms of at least 87.5% in terms of execution time.Second, MRIM provides an easy-to-use interface that exploits information provided by the R* algorithm.The experiments, conducted in real condition for the task of part replacement in a crowded environment, show that MRIM reduces considerably execution time and human errors.
Cathodic protection is a significant approach utilized to avoid the electrochemical corrosion of pipelines.This is accomplished by supplying an electric current to the structure that requires protection, such as a pipeline, from an external source.study aims to enhance the cathodic protection system by minimizing potential fluctuations along the pipeline hence preventing corrosion.It also aims to achieve economic feasibility by decreasing the number of anodes utilized.These objectives were accomplished by employing meta-heuristic optimization techniques.The present study involves formulating a mathematical model for a pipeline that provides fuel to the Al-Hilla 2 power plant in Iraq to assess the effectiveness of cathodic protection.Utilizing numerical simulation techniques, Multiphysics COMSOL, diverse scenarios are examined, resulting in the acquisition of substantial data.Subsequently, a neural network model is constructed using MATLAB.The primary factors influencing the distribution of cathodic protection potential are the numbers and positioning of the anodes and the output current.Subsequently, the optimization objectives involve determining the optimal anode number, position, and output current value by utilizing the Particle swarm organization (PSO) algorithm.The obtained results provide evidence that the proposed method holds a certain level of significance in guiding the design of cathodic protection systems.
Nowadays, deep learning models are used to summarize the large volume of text data to understand its intent effectively.Processing huge amounts of data can lead to an Information overload, where the models may generate text summaries that miss out on the important information of actual text content.Such problems in business document summaries can impact progressive business growth.This study employs a dataset comprising business articles sourced from BBC News to conduct an extensive comparative analysis of three prominent text summarization algorithms: Bidirectional and Auto-Regressive Transformers, Text-to-Text Transfer Transformer, and Pre-training with Extracted Gap-sentences for Abstractive Summarization, within the domain of business news summarization.The primary objective is to assess the accuracy of these models in generating concise and coherent summaries, utilizing ROUGE and METEOR scores as the benchmark for evaluation.Each model's proficiency in distilling business narratives while retaining crucial insights is carefully examined.This study analyzes the summaries generated and compares them with the already existing summaries.From the result analysis it observed that BART and PEGASUS show ROUGE-I score of 0.308 and 0.245, and METEOR score 0.28 and 0.25 respectively.The outcomes of this study show that the T5 excelled in the ROUGE-1 and METEOR scores which were 0.354 and 0.35 respectively.outcomes of this research offer significant implications for both researchers and practitioners, equipping them with advanced summarization techniques for extracting information effectively from business-related content.In an age where information overload is prevalent, the findings from this study can guide the selection and deployment of text summarization models to enhance information extraction processes, ultimately facilitating more efficient decision-making and information dissemination in the business domain.
The rise in agricultural innovation has led to the use of sustainable farming practices, such as aeroponics, which increase crop production.Aeroponics, a soil-free indoor precision farming system, cultivates crops using vertical towers, garnering global attention for its environmentally friendly and productive cultivation methods.Aeroponic systems can grow lettuce, a popular green-leafy vegetable, quickly and with minimal water usage.However, yield prediction is a tedious task in real-world scenarios.To efficiently predict lettuce yield, various scientific experiments have integrated IoT and machine-learning techniques.This research work utilized various machine-learning regression models, including linear, support vector, random forest, and XGBoost, to estimate lettuce yield based on specific growth parameters such as pH, EC, temperature, total dissolved salts (TDS), turbidity, humidity and light.After implementation, the results showed a high prediction accuracy of 93% and minimal error rates produced by the XGBoost regression model when compared with the other regression models.Further, fine-tuning the model parameters enhanced the XGBoost model's performance, enhancing its generalization capability to handle new realtime data.This indicates that optimizing the lettuce yield involves not only using indoor aeroponic farming methods but also utilizing advanced sustainable food production systems.
The advancement of artificial intelligence has deeply influenced numerous domains.One particular area that has experienced remarkable progress is natural language processing.This progress can be largely attributed to the widespread use and popularity of social media platforms.With the increasing use of social media, dialects have taken on a new importance, as the diversity of dialects has an important role to consider in the relevance of Natural Language Processing, as it allows a greater number of people to communicate using a pertinent and appropriate local context.As evidenced by the rise of Chatbots that allow people to interact with machines using their own native dialects.The significance of dialects, especially in the Arabic-speaking world, cannot be understated.Many Arabic dialects have been under-researched and not adequately addressed in natural language processing applications.Among these, the Moroccan dialect stands out, prompting researchers to focus their efforts on understanding and incorporating it into artificial intelligence technologies.To facilitate the development of Chatbots that can effectively understand and respond in Moroccan dialect, the availability of suitable datasets becomes vital.For this reason, we adopt a targeted strategy for creating datasets by exploiting the extensive resources offered by platforms such as YouTube, where audio content is highly diverse in terms of language.This involves classifying each audio according to its theme and dividing it into 30 second segments to simplify manual transcription into text.This meticulous process enabled us to accumulate and annotate a large volume of data.As a result, NLP models built on these extensive and comprehensive datasets can efficiently and accurately understand Moroccan dialect speech and text.With the aim to employ this dataset as training data for the future development of a Moroccan-dialect conversational Chatbot.The methodologies and techniques can be adapted and applied to other underexplored dialects, creating opportunities for further advancements in natural language processing in a global context.
Automatic ElectroEncephalogram EEG classification for Stress detection represents a crucial interest, simultaneously with the increasing deaths caused by depression and psychological effects.Accurate automatic classification of EEG signals represents a complex task, requiring the use of sophisticated algorithms.In this light, we focus through this work on achieving the automatic stress detection from EEG signals, to help clinicians to get the true diagnosis in an early stage.At this light, we opt through this paper to the implementation of a proposed Recurrent Neural Network RNN model for automatic stress detection.The proposed work employs a pre-processing combined with Recurrent Neural Network models such as Gated Recurrent Unit (GRU).We have applied the FFT transformation on EEG signals, available from Kaggle.The EEG classification results have reached 97.23% for train, 93.68% for validation and 88.86% for the test process, by implementing GRU based SGD optimizer networks.To get more accurate results, Adam optimizer has been implemented, achieving results equal to 99.53%, for the train, 94.98% for the validation and 89% for the test process.Moreover, stress emotions have been well detected as demonstrated by the confusion matrix results.Finally, accuracy and loss curves show promising results for both training and validation and the error rate is too close to zero.Our proposed RNN model, with its reduced number of parameters shows to be an excellent application to be implemented on embedded systems, thanks to its lightweight reducing both training time and memory consumption.
Cloud computing offers many benefits, but it also presents new cybersecurity challenges that must be addressed to ensure data protection in the cloud environment.Governments and organizations face increasing and ongoing cyberattacks by state-sponsored hackers to wage cyberwars.A successful cyberattack on vital infrastructure, such communications or electricity networks, might have disastrous effects.These attacks vary in their forms and patterns, which makes understanding and confronting them necessary.An essential function of artificial intelligence is the detection of intrusions.To prevent various cyberattacks in the cloud environment and is widely considered the best method.The Deep Learning (DL) method efficiently trained on datasets to improve performance based on statistical features can accurately detect various attacks.In this paper, we use the CE-CIC-2018 dataset that contains seven distinct attack scenarios, updated for cybersecurity: Brute-force, Heartbleed, Botnet, DoS, DDoS, and Web Attacks.This paper contributes, to improving the precision of identifying different types of threats in a cloud environment and improving additional performance indicators.The proposed using DL method dimensionality reduction using Principal Component Analysis (PCA), the Fuzzy C-Means (FCM) technique to create clusters, and the deep learning-based AutoEncoder (AE) method combined to identify the attack and non-attack.PCA + FCM + AE method prevents different cyberattacks in a cloud environment.The results showed that the best accuracy was 97.70 %, which is the highest accuracy compared to those results reported in the relevant literature.
Aspect-based sentiment analysis aims to classify sentiment polarity in opinionated texts based on its associated aspect.However, imbalanced data is a significant challenge that can lead to a decline in classification performance.In machine learning, strategies such as oversampling and undersampling can be implemented to rectify this imbalance.The primary objective of this study is to investigate the impact of data balancing techniques, including oversampling and undersampling, on aspect-based sentiment analysis to enhance classification performance.To achieve this objective, SMOTE, random oversampling, and random undersampling are employed in logistic regression for multi-label classification in aspect-based sentiment analysis.The data for this study was obtained from Google Reviews submitted by individuals who visited the beach in Indramayu.Subsequently, this data was annotated based on tourism-related factors and the sentiments expressed by users.Following this, the data underwent a preprocessing stage and was divided into separate training and test datasets.The training dataset accounted for 60% of the data, while the remaining portion was allocated for testing purposes.During the model training process, data balancing was achieved by implementing oversampling and undersampling techniques and utilizing Logistic Regression with Stochastic Gradient Descent Optimization as the model learning method.The resultant model was subsequently employed to test the test dataset.The evaluation results indicate that oversampling techniques led to a considerable improvement in performance compared to the absence of data balancing.These findings provide a comparison between balancing techniques in sentiment analysis models in tourism that suffer from an imbalanced dataset.Consequently, the oversampling technique can be considered in developing aspect-based sentiment analysis models within the tourism industry.
In many fields, such as monitoring the environment, health care, and surveillance, Software Defined Networking (SDN) was essential.The system performance of SDN remains severely constrained by the restricted electrical sources available.Most of the applications being developed in such networks are meant for critical a service which requires high computation leading to power dissipation and energy consumption.In such constraint networks, energy consumption is a significant cost factor for computing resources.Formation of cluster, Route establishment and transmission of data are the three aspects of the proposed technique.This paper offers a hierarchical navigation method for SDN, depending on the Improved Lion Optimization (ILO) method to handle this problem while improving the network's lifespan and Quality of Service (QoS) by using less power.The ILO method is used to create sensor node clusters according to an organizational framework during the cluster-formation phase.Simulations on computers have been employed to examine the proposed strategy, and several protocols for routing in use.Evaluated for their potential to improve SDN, efficiency and lengthen the lifespan of the network.The outcomes show that the proposed method can locate the shortest way, reduce expenditures, and minimize the use of energy.In addition, in comparison with existing methodologies, the proposed strategy delivers a longer network lifespan and higher QoS.
In the process of the K-means clustering algorithm, one of the issues that arises is the high number of iterations.This study aims to optimize the cluster evaluation results in K-means by reducing iterations through the application of the Weight Product Model (WPM).The evaluation method used in this research is the Davies-Bouldin Index (DBI).Three datasets were analyzed: the QSAR Dataset consisting of 908 data points, 7 attributes; the Whoscale Customer dataset consisting of 440 data points, 8 attributes from the UCI Machine Learning Repository, as well as direct observational data from captured fisheries obtained from the North Aceh District Office of Marine and Fisheries, Indonesia consisting of 75 data points, 8 attributes.The results of 10 testing iterations on three different datasets show that for the QSAR Dataset, the average cluster evaluation using DBI with K-means is 0.852.However, when applying WPM+K-means, the average DBI value increases to 0.727, with the average number of K-means iterations reduced from 23 to 8 iterations.For the Whoscale Customer dataset, the average cluster evaluation using DBI with K-means is 0.921.In contrast, when employing WPM+K-means, the average DBI value slightly improves to 0.910, accompanied by a reduction in the average number of K-means iterations from 23 to 10 iterations.In the case of the captured fisheries dataset, the average cluster evaluation using DBI with K-means yields a value of 1.222.However, implementing WPM+K-means results in an improved average DBI of 1.052.Furthermore, the average number of K-means iterations is reduced to 9 iterations, whereas for WPM+K-means, this number is reduced to 4 iterations.The results of this study demonstrate an improvement in DBI values, where lower DBI values indicate better performance of the K-means algorithm.These also findings demonstrate that WPM is effective in optimizing cluster evaluation values in Kmeans clustering.With the reduction in the number of K-means iterations, computational time is expected to be faster.
