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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Kajendran, K. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This research analyzes the effectiveness of the K-Nearest Neighbors algorithm combined with moving average techniques, Five-day and Ten-day Exponential Moving Averages, specifically EMA-5 and EMA-10, to predict stock prices. The study will focus on the adjusted closing price to deal with the autocorrelation issue that is common in time series data. The performance of the models was evaluated based on four key metrics: mean absolute error, mean squared error, root mean square error, coefficient of determination, overfitting risk test and generalization capability test. It was shown from the results that KNN models integrated with EMA-5 and EMA-10 offered high accuracy, one of which, with an R2 coefficient indicating the capacity to capture stock price trends with an accuracy of up to 99%. The research pointed out how short-term EMAs, combined with machine-learning algorithms, can substantially improve stock price prediction accuracy. It gives useful insights into stock price behaviour, therefore providing a decisive precedent for future works in creating a viable method for selecting trading strategies using machine-learning tools and moving averages. © 2025 IEEE.

关键词： Time series

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Integrating ARIMA with Simple Moving Averages for Predictive Stock Price Modeling 1

Integrating ARIMA with Simple Moving Averages for Predictive...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Forecasting stock prices is crucial because even minor incremental improvements translate into significant advantages in their financial decisions. A novel method that combines ARIMA Models with Simple Moving Averages (SMAs)is proposed to enhance prediction accuracy. SMAs for 5 and 10 days were adopted as exogenous variables in the ARIMA structure, achieving a phenomenal accuracy of 99 percent. The performance of the model was analyzed using statistical measures such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2). The prediction is reaffirmed as valid by obtaining lower RMSE values while concurrently having high R2 scores. Graphical tools include the plot of the scatter plot, the plot of residuals, and the histogram of error distribution for examining model efficiency. This joint ARIMA-SMA modeling approach is highly effective and potentially a powerful tool for financial analysts and decision-makers: predicting stock prices with 99% accuracy might well represent a new horizon toward which researchers can steer predictive modeling in stock market analysis. © 2025 IEEE.

关键词： Prediction models

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Leveraging Random Forests for Stock Market Bubble Identification: A Comparative Analysis 1

Leveraging Random Forests for Stock Market Bubble Identifica...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This research attempts to identify stock market bubbles using technical indicators combined with machine-learning processes. If not detected early on, stock bubbles arising from overvaluation and speculation can incur huge losses. The study used Technical Indicators such as the Relative Strength Index(RSI), Moving Average Convergence Divergence(MACD), Bollinger Bands, and Volume Indicators to catch the market trends. These features were analyzed using Support Vector Machine, Logistic Regression, and Random Forest models with a 99.84% test accuracy and AUC equal to 1.0 obtained with Random Forest, which proved to be, in this research, the most reliable for bubble detection. The study compares, in detail, the results, including Confusion Matrices and Receiver Operating Characteristic curves, validating the findings. The approach here would be a robust and data-driven framework for investors and analysts to recognize bubbles, facilitating better decision-making by risk minimization. The results also contribute to further developments in the role of machine learning in equity markets. © 2025 IEEE.

关键词： Marketplaces

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer Science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks

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computers, Materials & Continua 2023年第4期75卷 1331-1351页

作者： Anubha Ravneet Preet Singh Bedi Arfat Ahmad Khan Mohd Anul Haq Ahmad Alhussen Zamil S.Alzamil Department of Computer Applications I.K.G.Punjab Technical UniversityKapurthala144601PunjabIndia Research and Development I.K.G.Punjab Technical UniversityKapurthala144604PunjabIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of *** main challenges are related to routing techniques that are generally employed in *** Adhoc system management,on the other hand,requires further testing and improvements in terms of *** routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two *** main aim of this research work is to determine the optimum method for sending packets while also extending life time of the *** is achieved by changing the residual energy of each network ***,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are ***,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of *** simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.

关键词： Routing optimization reward penalty mobility energy throughout pheromone

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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computer Systems Science & engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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TOWARDS THE FUNDAMENTAL LIMITS OF KNOWLEDGE TRANSFER OVER FINITE DOMAINS 12

TOWARDS THE FUNDAMENTAL LIMITS OF KNOWLEDGE TRANSFER OVER FI...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhao, Qingyue Zhu, Banghua Department of Computer Science and Technology Tsinghua University China Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

We characterize the statistical efficiency of knowledge transfer through n samples from a teacher to a probabilistic student classifier with input space S over labels A. We show that privileged information at three progressive levels accelerates the transfer. At the first level, only samples with hard labels are known, via which the maximum likelihood estimator attains the minimax rate √|S||A|/n. The second level has the teacher probabilities of sampled labels available in addition, which turns out to boost the convergence rate lower bound to |S||A|/n. However, under this second data acquisition protocol, minimizing a naive adaptation of the cross-entropy loss results in an asymptotically biased student. We overcome this limitation and achieve the fundamental limit by using a novel empirical variant of the squared error logit loss. The third level further equips the student with the soft labels (complete logits) on A given every sampled input, thereby provably enables the student to enjoy a rate |S|/n free of |A|. We find any Kullback-Leibler divergence minimizer to be optimal in the last case. Numerical simulations distinguish the four learners and corroborate our theory. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Students

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The Impact of SMOTE and ADASYN on Random Forest and Advanced Gradient Boosting Techniques in Telecom Customer Churn Prediction 10

The Impact of SMOTE and ADASYN on Random Forest and Advanced...

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10th International Conference on Web Research, ICWR 2024

作者： Imani, Mehdi Ghaderpour, Zahra Joudaki, Majid Beikmohammadi, Ali Stockholm University Department of Computer and System Sciences Stockholm10691 Sweden Ayatollah Boroujerdi University Electrical and Computer Engineering Boroujerd Iran University of Kashan Electrical and Computer Engineering Kashan Iran Stockholm University Department of Computer and Systems Sciences StockholmSE-16455 Sweden

ISBN: (纸本)9798350394986

This paper explores the capability of various machine learning algorithms, including Random Forest and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection;following the application of SMOTE and ADASYN sampling methods;and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and *** outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post-ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies. © 2024 IEEE.

关键词： Machine learning

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Impact of Human Vulnerabilities on Cybersecurity

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computer Systems Science & engineering 2022年第3期40卷 1153-1166页

作者： Maher Alsharif Shailendra Mishra Mohammed AlShehri Department of Information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia

Today,security is a major challenge linked with computer network companies that cannot defend against *** vulnerable factors increase security risks and cyber-attacks,including viruses,the internet,communications,and *** of Things(IoT)devices are more effective,and the number of devices connected to the internet is constantly increasing,and governments and businesses are also using these technologies to perform business activities ***,the increasing uses of technologies also increase risks,such as password attacks,social engineering,and phishing *** play a major role in the field of *** is observed that more than 39%of security risks are related to the human factor,and 95%of successful cyber-attacks are caused by human error,with most of them being insider *** major human factor issue in cybersecurity is a lack of user awareness of cyber *** study focuses on the human factor by surveying the vulnerabilities and reducing the risk by focusing on human nature and reacting to different *** study highlighted that most of the participants are not experienced with cybersecurity threats and how to protect their personal ***,the lack of awareness of the top three vulnerabilities related to the human factor in cybersecurity,such as phishing attacks,passwords,attacks,and social engineering,are major problems that need to be addressed and reduced through proper awareness and training.

关键词： Cybersecurity phishing attack password attack social engineering cybersecurity awareness security risk

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CounterFair: Group Counterfactuals for Bias Detection, Mitigation and Subgroup Identification 24

CounterFair: Group Counterfactuals for Bias Detection, Mitig...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Kuratomi, Alejandro Lee, Zed Tsaparas, Panayiotis Junior, Guilherme Dinis Pitoura, Evaggelia Lindgren, Tony Papapetrou, Panagiotis Stockholm University Department of Computer and Systems Sciences Sweden University of Ioannina and Archimedes/Athena RC Department of Computer Science and Engineering Greece

ISBN: (纸本)9798331506681

Counterfactual explanations can be used as a means to explain a models decision process and to provide recommendations to users on how to improve their current status. The difficulty to apply these counterfactual recommendations from the users perspective, also known as burden, may be used to assess the models algorithmic fairness and to provide fair recommendations among different sensitive feature groups. We propose a novel model-agnostic, mathematical programming-based, group counterfactual algorithm that can: (1) detect biases via group counterfactual burden, (2) produce fair recommendations among sensitive groups and (3) identify relevant subgroups of instances through shared counterfactuals. We analyze these capabilities from the perspective of recourse fairness, and empirically compare our proposed method with the state-of-the-art algorithms for group counterfactual generation in order to assess the bias identification and the capabilities in group counterfactual effectiveness and burden minimization. © 2024 IEEE.

关键词： Algorithmic Fairness Counterfactual explanations Group counterfactuals Local explainability

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