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Information Technology (ACIT)

作者： Hani Attar Jafar Ababneh Ali Rachini Ramy M. Bahy Mohamed Hafez Ahmed Solyman Faculty of Engineering Zarqa University Zarqa Jordan Cyber Security Department Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science and Information Technology Holy Spirit University of Kaslik (USEK) Jounieh Lebanon Technical Research Center Cairo Egypt Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Underwater communication systems have garnered significant interest due to the limitations of the wireless spectrum and the growing need for sustained, covert communication capabilities. However, existing systems face significant challenges from doubly dispersive channels that cause time and frequency fading, disrupting the orthogonality of conventional OFDM subcarriers and leading to severe inter-symbol interference (ISI), even with complex equalizers like MMSE. This paper proposes a Fractional Fourier Transform (FrFT)-based OFDM system, offering better adaptability in the time-frequency domain. The FrFT-OFDM system effectively concentrates power around the diagonal of the channel matrix, mitigating Doppler shifts and multipath fading, thus enhancing spectral efficiency and robustness. A low-complexity equalizer is introduced, reducing computational demands while maintaining high performance, ensuring practical real-time application in underwater environments. Simulation results show that the proposed DFrFT-OFDM system with the optimized equalizer surpasses traditional OFDM under severe channel dispersion, demonstrating improved bit error rates and signal-to-noise ratios by about 3 db. This confirms its capability for reliable underwater communication with reduced computational overhead by about 90% comparing the OFDM with MMSA and DFrFT-OFDM with bande MMSA, marking a significant step forward for high-performance data transmission in challenging aquatic environments.

关键词： Fading channels Underwater communication Time-frequency analysis Equalizers OFDM Simulation Bit error rate Underwater acoustics Dispersion Signal to noise ratio

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques

Predictive Analysis of Consumer Behavior Using Supervised Le...

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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： K. Sashi Rekha T. Amutha R. Usharani S. Pushparani V.M. Sivagami Department of Computer Science and Engineering SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace.

关键词： Training Adaptation models Consumer behavior Supervised learning Data preprocessing Reinforcement learning Feature extraction Data models Predictive analytics Stress

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Adaptive Optimized Consensus control for a Class of Nonlinear Multi-Agent Systems with Asymmetric Input Saturation Constraints and Hybrid Faults

SSRN

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SSRN 2023年

作者： Tang, Fanghua Wang, Huanqing Zhang, Liang Xu, Ning Ahmad, Adil M. College of Control Science and Engineering Bohai University Liaoning Jinzhou121013 China College of Mathematical Science Bohai University Liaoning Jinzhou121013 China Institute of Information and Control Hangzhou Dianzi University Hangzhou310018 China Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

This article studies the adaptive optimized leader-follower consensus control problem for a class of discrete-time multi-agent systems with asymmetric input saturation constraints and hybrid faults based on the optimized backstepping technique. Different from the conventional saturation model, we consider an individual asymmetric saturation constraint for each actuator instead of a common upper and lower bound for all actuators. Besides, due to the complexity and scale of the actuators, a set of hybrid faults is also considered, with the main focus on the partial fault and bias fault for the multi-agent systems. To eliminate the effects of saturation and faults, a simplified smooth function is constructed to approximate the asymmetric saturation model, and the designed compensation signals are used to cope with the two main fault types such that the fault-tolerance and control performance of the multi-agent systems can be effectively improved. Subsequently, integrating all mechanisms into an adaptive optimized backstepping design framework, the long-term strategic utility functions and the virtual control signals generated in the backstepping procedure are approximated to the desired results by adopting the radial basis function neural network (RBFNN) and the actor-critic framework of reinforcement learning, and the weights in the actor-critic network are adjusted in the light of a classic gradient descent method. According to the forward difference Lyapunov function approach, it is proved that the closed-loop system can be stabilized and all errors are semiglobally uniformly ultimately bounded. Finally, the validity of the proposed control scheme is demonstrated through a simulation example. © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： R. Usharani V. M. Sivagami K. Saravanan S. Pushparani K. Sashi Rekha Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations.

关键词： Cloud computing Technological innovation Costs Biological system modeling Machine learning Predictive models Maintenance

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration

Exploring the Effectiveness of Deep Learning in Audio Compre...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： S. Pushparani K. Sashi Rekha V. M. Sivagami R. Usharani M. Jothi Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case.

关键词： Deep learning Training Filtering Bit rate Noise Computer architecture Feature extraction

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Design and Implementation of Mobile-based Application for Appointments Systems for Vaccinations in Medical Centers

Design and Implementation of Mobile-based Application for Ap...

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International Conference on Computer Theory and Applications (ICCTA)

作者： Mohammad M. Qabajeh Shima Mousa Hamam Abo Saleh Jamila Abo Hasan Department of Applied Computing Faculty of Applied Science Palestine Technical University Kadoorie Arroob Palestine

ISBN: (数字)9781665478540

ISBN: (纸本)9781665478557

The current patient registration and operating procedure in medical centers are time-consuming and somehow tiresome for patients and their families. Normally, patients have to visit the medical center or hospital to register, they have to bring their medical records, and wait to be called by the medical staff. The situation becomes worse when the patients are kids because they cannot wait for long time and their parents do not have enough time to wait for long time. In view of these problems, several appointment applications are proposed to enhance the registration systems and reduce the waiting time. However, these efforts are still face some drawbacks and limitations. In this paper, we have designed an Android-based mobile application to create appointments with doctors in medical clinics and store the patient’s health records. This mobile application is integrated with an interactive website and a comprehensive database to enable the doctors to handle vaccinations appointment, maintain and retrieve patients’ medical records. The managers also can efficiently create, manage and admin the clinic’s work progress. This mobile application will enhance appointment scheduling and improve the quality of healthcare. In addition, the application can reduce the time and effort consumed to perform the vaccinations appointment process and taking the vaccinations on time.

关键词： Processor scheduling Hospitals Databases Operating systems Vaccines Mobile applications Registers

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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Computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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Perturbations and Phase Transitions in Swarm Optimization Algorithms

arXiv

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arXiv 2025年

作者： Vantuch, Tomáš Zelinka, Ivan Adamatzky, Andrew Marwan, Norbert Center ENET Technical University of Ostrava Czech Republic Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical & Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science Technical University of Ostrava Czech Republic Unconventional Computing Lab UWE Bristol United Kingdom Leibniz Association Potsdam Germany

Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks. We demonstrate that swarm optimization algorithms also exhibit transitions from chaos, analogous to a motion of gas molecules, when particles explore solution space disorderly, to order, when particles follow a leader, similar to molecules propagating along diffusion gradients in liquid solutions of reagents. We analyze these ‘phase-like’ transitions in swarm optimization algorithms using recurrence quantification analysis and Lempel-Ziv complexity estimation. We demonstrate that converging iterations of the optimization algorithms are statistically different from non-converging ones in a view of applied chaos, complexity and predictability estimating indicators. An identification of a key factor responsible for the intensity of their phase transition is the main contribution of this paper. We examined an optimization as a process with three variable factors – an algorithm, number generator and optimization function. More than 9.000 executions of the optimization algorithm revealed that the nature of an applied algorithm itself is the main source of the phase transitions. Some of the algorithms exhibit larger transition-shifting behavior while others perform rather transition-steady computing. These findings might be important for future extensions of these algorithms. © 2025, CC BY-NC-ND.

关键词： Cellular automata

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Optimization of Expert Solutions for Events Forecasting

Optimization of Expert Solutions for Events Forecasting

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International Conference on control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Merab Pkhovelishvili Natela Archvadze Lia Shetsiruli Faculty of Informatics and Control Systems Georgian Technical University Tbilisi Georgia Department of Computer Science Ivane Javakhishvili Tbilisi State University Tbilisi Georgia School of Business and Management Grigor Robakidze University Batumi Georgia

Expertise is a universal method for solving managerial decision problems and is used in cases where there are no (or not found) criteria that allow solving the problem optimally or rationally. At its core, the examination is based on the use of human experience and is carried out with the involvement of experts. Recently, expert evaluations have been intensively used in various fields, especially in the prediction of tectonic natural disasters, because statistical data in this field are very few or difficult to obtain and analyze. Our article discusses the initial and subsequent criteria for determining the competence of experts, what influence the competence of an expert has in calculating his rank, and how these ranks are used for the conclusion of a group of experts, if this or that event will occur or what is the percentage of the event's occurrence.

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