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3rd International Conference on Computing and information Technology, ICCIT 2023

作者： Alasiri, Razan Ahmed Qahmash, Ayman King Khalid University College of Computer Science Depaertment of Information Systems Saudi Arabia

ISBN: (纸本)9798350321487

Forecasting the stock market is essential for investors to help assess the potential financial movement of the market value. Therefore, accurate estimation of the movement and price of the stock contributes more to the profit of investors through making informed investment judgments. Thus, the popularity of forecasting the stock market increased because of the difficulty of this field and that it is unforecastable and cannot be controlled because of its volatile nature and is affected by several factors. Nevertheless, the field of machine learning succeeded in forecasting the future stock market based on the analysis of time series data collected over time. The main objective of this paper is to develop day-to-day forecast models in ways that have been popular among researchers through time series data collected from the Saudi stock market, Tadawul, containing daily observations of the Tadawul All Share Index (TASI), and a different sample of some companies listed under the TASI. In our analysis, we relied on the technical approach, so it was important to choose technical indicators most closely related to the closing price of the next day using feature engineering and feature selection. Then, this study conducted two different models of Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) time series techniques. Best expected results Performance was evaluated using the Root Mean Square Error (RMSE) scale. The results show that LSTM algorithms based on deep learning are better than the ARIMA statistical model in stock market forecasting. © 2023 IEEE.

关键词： Long short-term memory

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Automated Control System of a Modular Robot Manipulator for Sorting Objects Based on Neural Networks 5

Automated Control System of a Modular Robot Manipulator for ...

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5th International Conference on Control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Zhitenko, Maxim Polyakov, Maxim Volgograd State University Depart. of Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350309126

This article is devoted to the development of an automated robot manipulator control system to sort objects based on the use of neural networks. This article discusses the processes associated with the creation of an automated control system and the development of its components. Specifically, a system is described that is designed to detect, classify, and determine the position of objects, as well as a system that calculates the rotation angles of the robot manipulator modules. The principles of operation of the control system and its components, as well as the architecture of neural networks used in their development are given. The results of the experiments demonstrate the effectiveness of using neural networks to solve the tasks of automating the control of robot manipulators. The accuracy of object recognition on the scene was achieved equal to 82.7 percent for a test sample of data. In addition, the selected neural network architecture ensures the positioning accuracy of the system equal to 99.7 percent with an acceptable error of 1 degree. © 2023 IEEE.

关键词： Intelligent systems

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Improved k-NN Regression Model Using Random Forests for Air Pollution Prediction

Improved k-NN Regression Model Using Random Forests for Air ...

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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Sharma, Siddharth Lakshmi, L. Rajya Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India

ISBN: (纸本)9798350302523

In this paper, we review various k-Nearest-Neighbor (k-NN) based models and their accuracies to develop a better model to predict concentrations of air pollutants. The proposed model splits the range of target variable values into a number of buckets first. Then, a hybrid k-NN model, which is a combination of weighted attribute k-NN and distance-weighted k-NN, and where the weights are assigned by calculating information Gain, is used for each attribute, to calculate the target variable value of each test case. The proposed model decreases the root mean square error (RMSE) of predicted NO, NO2 and NOx values by 28.29%, 29.44%, and 16.51% respectively, compared to the state-of the-art. Similarly, the mean absolute error (MAE) values for NO, NO2, and NOx are decreased by 18.26%, 33.67%, and 14.54%, compared to the state-of the-art. This model gives good results when the size of each bucket is nearly equal. © 2023 IEEE.

关键词： Air pollution

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Highlighting of Hydrographic Objects on Satellite Images for the Development of High-quality Digital Relief Models of Floodplain Areas as a Basis for Modeling the Hydrological Regime 5

Highlighting of Hydrographic Objects on Satellite Images for...

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5th International Conference on Control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Tertychnaya, Anna Khoperskov, Alexander Volgograd State University Dept. Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350309126

Mathematical modeling of surface water dynamics allows us to make forecasts of the hydrological regime of a territory for a wide variety of hydrological, environmental, and geophysical applications. The such simulations quality is largely determined by the relevance of the topographic data used to construct digital terrain models. The important task is the identifying of the boundaries of numerous reservoirs in a floodplain area. The distinctive feature such area is the complex structure of small channels, vegetation, and wetlands. We develop the method for detecting coastlines using multi-channel satellite data which includes two infrared bands (4 and 5 bands), such as Landsat ETM+. Software was tested whith the Northern part of the Volga-Akhtuba Floodplain (Russia) and one of the region in the Amazon basin. It differs significantly in their natural characteristics. During the software testing we determined the conditions that provides accurate detection of borders of water bodies. © 2023 IEEE.

关键词： Reservoirs (water)

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Lung Cancer Detection Using Machine Learning and Deep Learning Models 21

Lung Cancer Detection Using Machine Learning and Deep Learni...

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21st International Multi-Conference on systems, Signals and Devices, SSD 2024

作者： Alheeti, Khattab M. Ali Al-Shouka, Tabreer T. Majeed, Saleem Hamad Ahmed, Amjed Abbas College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq College of Computer and Information Technology University of Anbar Computer Sciences Department Anbar Iraq Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9798350374131

Technology and artificial intelligence play a significant role in improving healthcare and enable tasks to be automated. In addition, the diseases can be better understood and diagnosed faster, saving time and reducing costs. This study examines the impact of transfer learning models on the effectiveness of deep learning models in classifying lung cancer through the analysis of CT scan images. Additionally, it investigates the relative performance of various machine learning and deep learning models, encompassing Support Vector Machine (SVM) and convolutional neural net-works (CNN) such as InceptionV3, VGG16, Xception, ResNet50, and MobileNetV2, in the early detection of lung cancer based on CT scan images. The SVM model achieved an overall accuracy of 89% after preprocessing, the proposed approach was applied to five pre-trained models (ResNet50, In-ceptionV3, VGG16, Xception, MobileNetV2) using the dataset: Chest CT-Scan;Among the pre-trained CNN models, the Mo-bileNetV2 model achieved the highest accuracy of 98% and the lowest test loss, indicating it performed the best. The Xception model achieved the second-highest accuracy of 97%. The image pre-processing phase plays a significant role in im-proving system performance in terms of improving image contrast and increasing processing speed. © 2024 IEEE.

关键词： Support vector machines

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Machine Learning based Crop Yield Prediction in Agriculture: A Survey in Vellore

Machine Learning based Crop Yield Prediction in Agriculture:...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Durai Raj Vincent, P.M. Elavarasan, Dhivya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350325188

Agriculture is the backbone of any country as it feeds its population. Since the global population keeps increasing, it is necessary to give utmost importance to the field of agriculture. State-of-the-art techniques should be introduced to boost agricultural production multifold. On the other hand, machine learning brings new life to the computing field, which would solve many complex problems in every other field. Machine learning in agriculture based crop yield prediction is the focus of this paper, where significant works are to be explored in this work to understand the commendable outcomes. For this survey, the research articles that have been published in the last five years where vellore a southern Indian district based works are selected and the various aspects of the underlying research and its applications are listed as the outcome of this for the benefit of future budding researchers who would be working in this field of agriculture. © 2023 IEEE.

关键词： climatic conditions crop yield prediction Machine Learning rainfall data Reinforcement Learning

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Predicting the effect of genes on longevity with novel hierarchical dependency-constrained tree augmented naïve Bayes classifiers

Predicting the effect of genes on longevity with novel hiera...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wan, Cen Birkbeck University of London Department of Computer Science and Information Systems London United Kingdom

ISBN: (纸本)9798350337488

Understanding the roles of ageing-related genes is crucial for deciphering the mystery of ageing. In this work, we propose a novel hierarchical dependency-constrained tree augmented naïve Bayes algorithms, i.e. Hie-TAN-Lite, to classify model organisms' genes into pro-longevity or anti-longevity genes. The method exploits the pre-defined generalisation-specialisation relationships between Gene Ontology terms as a type of constraint to learn the tree representation of dependencies among Gene Ontology terms, and also eliminates the hierarchical redundancy when learning the classifier. The experimental results showed that Hie-TAN-Lite successfully obtained better predictive performance than several other hierarchical dependency-constrained classification algorithms. © 2023 IEEE.

关键词： Ageing biology Classification Gene Ontology Hierarchical feature spaces Tree augmented naïve Bayes

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Unveiling Veracity: An Integrated Semi-Supervised Algorithm for Fake News Detection 16

Unveiling Veracity: An Integrated Semi-Supervised Algorithm ...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Malik, Aman Behera, Dayal Kumar Swain, Amulya Ratna Hota, Jhalak Birla Institute of Technology and Science Department of Computer Science & Information Systems Hyderabad India Kiit University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350371154

In recent years, the pervasive dissemination of misinformation and deliberately falsified content, commonly referred to as 'fake news,' has become a critical challenge in the realm of information dissemination. With the exponential growth of digital media and online platforms, the ease and speed at which misleading information can be generated and spread have increased substantially. Addressing the formidable task of detecting fake news has thus become an imperative within the field of information technology. The supervised model gives promising results. However, the availability of labelled data is critical in the realm of fake news detection. Leveraging unlabeled data alongside labelled data is an effective approach, but it requires careful handling. This paper explores the application of Semi-Supervised Learning for fake news detection by integrating Label Propagation and various ML models as base classifiers. By leveraging both labelled and unlabeled data, Label Propagation with CatBoost Classifier seeks to overcome traditional limitations, offering a promising avenue for bolstering the reliability of automated systems in distinguishing between authentic and deceptive information in the vast landscape of digital content. © 2024 IEEE.

关键词： information dissemination

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Modeling of Extended Flooding of the Volga-Akhtuba Floodplain Under Various Conditions

Modeling of Extended Flooding of the Volga-Akhtuba Floodplai...

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2023 Applied Mathematics, Computational Science and Mechanics: Current Problems, AMCSM 2023

作者： Klikunova, Anna Yu. Dyakonova, Tatyana A. Agafonnikova, Ekaterina O. Volgograd State University Dept. Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350348576

A computer model of a extended flood due to an accident at a hydraulic structure is considered. The model is based on the two-dimensional Saint-Venant equations, taking into account the friction and the structure of the terrain on the right side. The integration of the equations is carried out using the Lagrangian-Eulerian CSPH-TVD method (Combined Smooth Particle Hydrodynamics - Total Variation Diminishing). Modeling of various scenarios of the destruction of the Volga hydroelectric dam on the dry bottom during the fall and the wet bottom during the seasonal flood was carried out. For calculations, a regular digital terrain model is used, taking into account the channels of the Volga, Akhtuba and the main floodplain peaks. The main parameters of the breakout wave are calculated. Integral characteristics of flooding for the northern part of the Volga-Akhtuba floodplain are obtained. It is shown that hydraulic friction influences the propagation velocity of the breakthrough wave. The simulation results for a given digital model confirm the hypothesis that Volgograd is located in a zone that is not subject to flooding. © 2023 IEEE.

关键词： Hydroelectric power plants

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