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Papers by Advances in Engineering and Intelligence Systems (AEIS)

Advances in Engineering and Intelligence Systems, 2025

Accurately measuring text similarity holds significant importance in various text-centric applica... more Accurately measuring text similarity holds significant importance in various text-centric applications, including text clustering, information retrieval, and question/answer systems. This study focuses on enhancing the precision of deep learning models in gauging text similarity. To achieve this, a novel hybrid approach is proposed, integrating a Siamese neural network with lexical similarity features. The Siamese network comprises two parallel sub-networks, each featuring a word embedding layer and a deep neural network. This study explores three variations of deep neural networks (CNN, LSTM, Bi-LSTM), alongside two types of word embedding models and lexical similarity features, constructing diverse models. Evaluation across three distinct datasets demonstrates the superiority of the hybrid Siamese neural network model, leveraging convolutional networks and lexical features, showcasing higher Pearson's correlation and lower mean square errors (MSE) compared to literature models. These results signify advancements in accurately assessing text similarity. The combined Siamese network model, incorporating a convolutional network, lexical features, and the cross-embedding layer (SNN_CNN_feat), achieved the highest correlation value (0.7590) and the lowest MSE error value (1.0235), as established.

Advances in Engineering and Intelligence Systems, 2025

The Kalman filter is a widely employed algorithm for state estimation and sensor fusion in variou... more The Kalman filter is a widely employed algorithm for state estimation and sensor fusion in various fields. However, its performance can degrade in the presence of modeling errors and uncertainties in the system dynamics. To enhance the robustness and accuracy of the Kalman filter, the concept of a Fading-Memory Kalman Filter (FMKF) has been introduced. The FMKF incorporates a fading-memory mechanism that effectively mitigates the impact of modeling errors and reduces permanent estimation errors. By assigning time-varying weights to past measurements and predictions, the FMKF adaptively adjusts its influence on the current state estimation. This mechanism allows the FMKF to better accommodate dynamic system behavior and parameter changes. In this paper, the effectiveness of the FMKF is evaluated by comparing it with the standard Kalman filter. The performance of both filters is assessed in scenarios where modeling errors are present, and parameter variations occur. The results demonstrate that the FMKF outperforms the standard Kalman filter by providing more accurate and robust state estimates, even in the presence of modeling errors. The FMKF's ability to adaptively update the weights based on the relevance of past information allows it to effectively handle dynamic system behavior and changing parameters.

Advances in Engineering and Intelligence Systems, 2025

Due to the ever-increasing amount of information and their detailed analysis, the problem of clus... more Due to the ever-increasing amount of information and their detailed analysis, the problem of clustering, which is used to reveal hidden patterns in data, is still of great importance. On the other hand, the clustering of important genetic data, which often have high dimensions, faces many limitations using traditional methods. In the current work, a new semi-supervised ensemble spectral clustering (EPLSC) algorithm based on the graph p-Laplacian for genetic data is introduced. In the proposed approach, we first propagate the pairwise must-linked as well as cannot-linked constraints on all data. Then the feature space is randomly split into various unequal subspaces. Using the updated pairwise constraints, semi-supervised spectral clustering is performed in each subspace independently. Then, using the results of each one, an adjacency matrix is created based on ensemble learning. Next, by using several search operators in environments composed of different subspaces, the best set of subspaces is obtained. Experimental validation on 15 high-dimensional genetic datasets demonstrates that EPLSC outperforms existing methods, achieving improvements of up to 18% in Normalized Mutual Information (NMI) and 15% in Adjusted Rand Index (ARI) compared to traditional semi-supervised techniques. This indicates that EPLSC not only enhances clustering efficacy but also effectively addresses the unique challenges posed by genetic data.

Advances in Engineering and Intelligence Systems, 2025

Spam emails constitute a significant percentage of email traffic and are considered a cybersecuri... more Spam emails constitute a significant percentage of email traffic and are considered a cybersecurity threat, often leading to phishing attacks, malware infections, and financial fraud. These emails, sent in bulk for commercial and malicious purposes, can bypass traditional spam filters, necessitating the development of high-accuracy models for effective detection. A major challenge in spam filtering is reducing false positives, which can lead to legitimate emails being incorrectly classified as spam, impacting users' email communication. In this study, deep learning (DL) and natural language processing (NLP) methods were employed to develop a spam detection model. Five DL-based models—Dense, CNN, LSTM, CNN-LSTM, and BERT—were evaluated. Data preprocessing included stemming, lemmatization, and text vectorization using Word2Vec to enhance feature extraction. The models were trained on a real dataset, and their accuracy was assessed using multiple evaluation indices. The findings demonstrated that, among the tested models, BERT achieved the highest accuracy (99.33%), outperforming all other approaches in spam detection. Its ability to understand contextual relationships and mitigate false positives makes it highly suitable for real-world applications. Given its computational demands, future research should focus on optimizing BERT for real-time deployment through model compression and parallel execution. Additionally, further testing on larger and more diverse datasets and implementing multilingual spam filtering capabilities will enhance its practical utility.

Advances in Engineering and Intelligence Systems, 2025

The Internet of Things (IoT) improves our lives by facilitating real-time communication between p... more The Internet of Things (IoT) improves our lives by facilitating real-time communication between people and objects. Predictive analytics could turn a reactive approach into a proactive one with the rise of artificial intelligence (AI) and machine learning in the healthcare sector. As a branch of machine learning, deep learning is able to deal with large amounts of data quickly, produce valuable insights, and solves complex problems. Accurate and timely diagnosis of diseases is essential for disease prevention and early treatment. The widespread adoption of electronic medical records necessitates the development of prediction models that are more accurate to effectively harness recurrent neural network variants of deep learning. This study investigates the integration of the Internet of Things and deep learning for disease prediction and diagnosis, which highlights key advancements in data collection, preprocessing techniques, and feature extraction strategies. The potentiality of convolutional and recurrent neural networks in increasing the accuracy of diagnosis and allowing early detection of diseases is analyzed. This review shows how IoT combined with deep learning can develop an investigative disruption in the prediction and diagnosis of diseases by expanding their validity, speed, and possibility of early detection thus opening the door to new applications and effective research.

Advances in Engineering and Intelligence Systems, 2025

This study explores the application of a machine learning model called Multi-Layer Perceptron Reg... more This study explores the application of a machine learning model called Multi-Layer Perceptron Regression (MLPR) for building cooling demand prediction. Through a hybridization technique with two cutting-edge optimization algorithms, the Brown Bear Optimization Algorithm (BBOA) and the Non-Monopolize Search Algorithm (NMSA), it sets out to explore its optimization potential. This leads to the development of optimized MLTN and MLBB models. The dataset was divided into 70% for training and 15% each for the rounds of testing and validation to make sure that the assessment is robust. Five insightful evaluation measures were utilized to assess the performance of the models, namely: MARE, MSE, RMSE, R2, and NRMSE. A model with the highest R2 and lowest error metric values across all phases is considered superior. Further, careful analysis of the layers of all three models constantly shows that the MLBB model is better compared to others, as seen by the highest R2 and lowest error values it has. In the third layer, the MLBB model gave a good performance, with an R2=0.999, RMSE=0.360, MSE=0.130, MARE=0.015, and NRMSE=0.001, which is really commendable. This goes to heighten the reliable effectiveness of MLBB in making precise predictions of building cooling load, hence it can be applicable in real life for encouraging energy-efficient building operations.

Advances in Engineering and Intelligence Systems, 2025

The anticipation of heating demands is critical in energy saving as well as emissions mitiga-tion... more The anticipation of heating demands is critical in energy saving as well as emissions mitiga-tion. Accurate forecasting of energy demands, combined with detailed studies on renovation possibilities, is essential in building management and is becoming increasingly important in both research and practical applications. Addressing this need, this study adopts a holistic ap-proach by integrating advanced optimization algorithms with precise heating load prediction techniques. Heating load systems present a complex landscape where energy optimization challenges require in-depth investigation and innovative problem-solving. To enhance predic-tive accuracy, two meta-heuristic algorithms, the Reptile Search Algorithm (RSA) and the Flow Direction Algorithm (FDA), are seamlessly integrated into the Adaptive Neuro-Fuzzy Inference System (ANFIS) model. These algorithms utilize heating load data collected and validated through prior stability tests. The study demonstrates that combining meta-heuristic optimization with ANFIS significantly improves prediction accuracy, offering a promising approach for more efficient energy management strategies. The research introduces three models: ANFD (ANFIS+FDA), ANRS (ANFIS+RSA), and an independent ANFIS model, each providing valuable insights for precise heating load prediction. It is important to consider that ANRS model is a notable performer with a great R2 measure equaling 0.991 in addition to a very small RMSE measure equaling 0.951. Such impressive performances reflect that ANRS model is a great predictor of heating load outcomes.

Advances in Engineering and Intelligence Systems, 2025

This research study delves into the domain of civil engineering, specifically focusing on the pre... more This research study delves into the domain of civil engineering, specifically focusing on the prediction of compressive strength (f'c) in Recycled Aggregate Concrete (RAC). As the construction industry seeks sustainable solutions, RAC has gained prominence due to its environmentally friendly nature. However, accurately predicting the f'c of RAC remains a complex challenge, owing to the inherent variability of recycled materials. To address this issue, robust hybrid machine learning (ML) approaches are employed, particularly emphasizing the Least Square Support Vector Regression (LSSVR) model. This investigation explores the integration of LSSVR with two innovative optimizers, namely the Giant Trevally Optimizer (GTO) and the Dingo Optimization Algorithm (DOA). The performance of the LSSVR model is enhanced through the utilization of these optimizers, resulting in its increased proficiency in predicting the f'c of RAC. Through comprehensive experimentation and rigorous analysis, this research showcases the effectiveness of the proposed hybrid method. The findings illustrate significant improvements in the accuracy and reliability of f'c predictions for RAC compared to traditional methods. Moreover, this investigation provides significant perspectives on the synergy between ML and optimization techniques in civil engineering. The most reliable outcomes in this study were achieved through the hybridization of the LSSVR model with GTO. The resulting LSGT model attained the highest R2 value of 0.989 and the lowest RMSE value of 1.618. Overall, the integration of LSSVR with GTO and DOA emerges as a promising methodology to enhance f'c prediction in RAC. This research enhances the current understanding of RAC and highlights its potential for robust hybrid machine-learning approaches in solving real-world challenges within civil engineering.

Advances in Engineering and Intelligence Systems, 2025

In this paper, some problems of electric vehicles (EVs) are simultaneously and comprehensively ex... more In this paper, some problems of electric vehicles (EVs) are simultaneously and comprehensively examined and solved. In the first step, after introducing EV technology as the future step for the transportation industry and discussing its challenges for the electricity industry, the vehicle to grid (V2G) is explained. Then, their advantages and requirements are discussed. In the second step, a novel method based on probabilistic techniques is proposed for forecasting the charge load curve of EVs in the electricity distribution grids. The stochastic distribution function parameters used in this method are estimated through processing the information obtained from the owners of the conventional EVs. To confirm the robustness of the proposed method, it is implemented on a small statistical population. In the third step, the time and level of battery charging are controlled by stochastic planning of the charging the hybrid EV (HEV) in the parking and via considering variable electricity prices. This stochastic planning aims to minimize the costs of charging EVs from the viewpoint of their owners. Moreover, EV parking can discharge the EVs’ batteries in high-price time intervals to meet a part of the grid’s demand. Results show that the costs and consumed load peak are considerably reduced due to the similarity between the load curve and the variable electricity market prices.

Advances in Engineering and Intelligence Systems, 2024

Compressive strength (CS) and slump flow (SL) are two of the most essential parameters in High-Pe... more Compressive strength (CS) and slump flow (SL) are two of the most essential parameters in High-Performance Concrete (HPC), which directly affect its structural capacity, durability, and workability. The following study represents an important step toward developing novel hybrid models for predicting CS and SL. The contribution in this paper proposes the following: the radial basis function (RBF) model will be enhanced by using two optimization algorithms, namely Horse Herd Optimization (HHO) and Wild Geese Algorithm (WGA). Accordingly, two hybrid models have been proposed, referred to as RBFH and RBWG. For model testing, comprehensive metrics include the coefficient of determination (R²), RMSE, MAE, VAF, and SI. It has the highest R² values of 98.17 for CS and 93.54 for SL predictions among all the datasets, while it also records the lowest SI values of 0.064 and 0.037 for CS and SL, respectively. These are indicative of the accuracy and reliability of the RBWG model in modelling the properties of HPC. This work's significance consists of improving the concrete mix design by giving correct predictions of HPC performance, which will lead to optimized resource utilization, minimized costs, and reduced negative environmental impacts due to construction. The results highlight hybrid machine learning models as the potential to solve complex challenges in civil engineering and provide new approaches toward sustainable and efficient infrastructure development.

Advances in Engineering and Intelligence Systems, 2024

Fly ash-containing concrete has only been the subject of a small amount of research focused on fo... more Fly ash-containing concrete has only been the subject of a small amount of research focused on forecasting the hardened concrete qualities. So little research has been done to predict the characteristics of self-compacting concrete in both its fresh and hardened states (SCC). Using support vector regression (SVR), it is planned to construct networks for estimating SCC's before and after hardening attributes. The goal of this research is to identify the SVR technique's critical parameters utilizing Henry gas solubility optimization (HGSO) and particle swarm optimization (PSO). SCC's fresh-phase characteristics include the slump flow, V-funnel test, and L-box test, whereas its hardened-phase features involve the strength of the compressive. The outcomes show tremendous promise for all assessed qualities in the assessment and development sections. In terms of development and assessment, it was clear that the presented networks have an excellent R^2 value. In other words, it signifies that the correlation between the real and anticipated characteristics of SCC from hybrid systems is satisfactory, which reflects superlative precision in the process of approximation and development. Overall, the HGSO-SVR model beats PSO-SVR, showing the capacity of the algorithm to choose the most effective parameters for the method under examination.

Advances in Engineering and Intelligence Systems, 2024

The widespread use of electronic devices in various applications has led to increased harmonic di... more The widespread use of electronic devices in various applications has led to increased harmonic distortion in power lines, resulting in a decreased power factor and reduced overall power quality. Voltage rectifiers connected to the line can be significant sources of this distortion. To mitigate these effects, it is crucial to enhance the power factor and minimize line current harmonics. This study employs a cascade-controlled boost converter for power factor correction, leveraging both voltage and current regulation. By integrating a self-tuning regulator (STR) adaptive controller with online identification within each control loop, this approach dynamically adjusts to fluctuations in current consumption over time. The proposed method effectively reduces Total Harmonic Distortion (THD) and improves power factor, contributing to more efficient and reliable power systems.

Advances in Engineering and Intelligence Systems, 2024

This paper introduces a novel methodology for predicting Unconfined Compressive Strength (UCS) in... more This paper introduces a novel methodology for predicting Unconfined Compressive Strength (UCS) in rocks by integrating Support Vector Regression (SVR) with two cutting-edge optimization algorithms: the Seahorse Optimizer (SO) and the COOT Optimization Algorithm (COOT). Unlike traditional UCS prediction methods that often struggle with slow convergence and local minima entrapment, this approach leverages the nonlinear modeling capabilities of SVR and enhances its performance through advanced optimizers. The SVSH (SVR+SO) and SVCO (SVR+COOT) models were developed and evaluated using a comprehensive dataset of rock samples with UCS measurements. Comparative analysis demonstrates that the proposed models not only achieve significantly higher prediction accuracy but also exhibit faster convergence compared to standalone SVR. These results underscore the potential of the hybrid SVR-optimizer models to set a new benchmark in UCS prediction, offering greater precision and computational efficiency. Among them, the SVSH models had the maximum accuracy with an excellent R2 value of 0.998 and a scanty RMSE value of 1.261. Therefore, the results confirm that the proposed SVSO model is a promising tool to be used by engineering and geological professionals. This ensures a very strong and reliable UCS prediction method that is vital for the improvement of civil engineering project safety and efficiency.

Advances in Engineering and Intelligence Systems, 2024

Precise anticipation of the ultimate bearing capacity (Qu)for rock-socketed piles is an important... more Precise anticipation of the ultimate bearing capacity (Qu)for rock-socketed piles is an important task in civil engineering, construction, and the design of foundations. The approach adopted here is new and solves the problem using KNN combined with two modern nature-inspired optimization frameworks, namely the Honey Badger Algorithm (HBA) and Equilibrium Slime Mould Algorithm (ESMA). The hybrid model in this paper combines the K-nearest neighbor with HBA and ESMA. The main objective was to improve the prediction performance of Qu for rock-socketed piles. This hybridization technique utilized the KNN model's strengths and two new optimizers to overcome the inherent uncertainty related to all variables that affect bearing capacity. These HBA and ESMA frameworks were proven capable of performing decent tuning of the KNN model, while their outcomes showed significantly improved predictive power. The hybrid model realized high accuracy for Qu estimation by considering various influencing parameters like soil properties, pile dimensions, and load conditions. The output of this study adds to the development in the area of geotechnical engineering by providing a sound methodology for Qu estimation in rock-socketed piles. The hybridization technique is a promising avenue for future exploration and practical applications, especially the KNHB frameworks that have obtained reliable outcomes by their very accurate R2 of 0.9945 and RMSE of 771.0886Outcomes support engineers and designers in formulating educated judgments concerning the foundation in soft soil environments. Ultimately, this exploration promotes safer and more efficient construction practices by minimizing risks associated with foundation failures.

Advances in Engineering and Intelligence Systems, 2024

The undrained shear strength (USS) of soil is essential in diverse structural engineering applica... more The undrained shear strength (USS) of soil is essential in diverse structural engineering applications, including the design of earth dams, rock fills, foundations, highways, railways, and slope stability analysis. Traditional empirical and theoretical methods for estimating USS based on field tests often rely on correlation assumptions, leading to imprecise results. These conventional strategies are also limited in terms of efficiency, both in time and cost. To address these limitations, this study introduces innovative machine learning techniques, employing the Support Vector Regression (SVR) model to accurately predict USS. To enhance model performance, three meta-heuristic optimization algorithms Differential Squirrel Search Algorithm (DSSA), Golden Section Search Optimization (GSSO), and Northern Goshawk Optimization (NGO) were utilized. The frameworks were trained using four key input metrics: plastic limit (PL), liquid limit (LL), sleeve friction (SF), and overburden weight (OBW). The performance of the proposed frameworks was evaluated using five criteria: R², RMSE, MSE, SI, and SMAPE. Among the three hybrid frameworks developed for estimating USS, the SVR optimized with the Northern Goshawk Optimization (SVNG) algorithm outperformed the others, achieving the lowest RMSE value of 39.30 in the testing step and the highest R² value of 0.9980 in the testing step. These results demonstrate the superiority of the SVNG model in providing precise and efficient predictions of USS, surpassing conventional methods.

Advances in Engineering and Intelligence Systems, 2024

In recent years, there has been a noticeable surge in population, accompanied by the rapid develo... more In recent years, there has been a noticeable surge in population, accompanied by the rapid development of industries, services, and agriculture within communities. This growth has intensified pressure on water resources, resulting in a simultaneous decline in both the quantity and quality of these vital resources. Dissolved organic carbon (DOC) stands out as a pivotal factor in determining the quality of natural freshwater bodies. It serves as a crucial indicator of water pollution, offering insights into the presence of both natural and man-made organic pollutants. This study focuses on predicting the levels of dissolved organic carbon in South Florida utilizing artificial intelligence algorithms. Specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Decision Tree (DT), and Support Vector Regression (SVR) algorithms were employed. The Sparrow Search Algorithm was utilized to fine-tune the hyperparameters of these algorithms. The effectiveness of the hybrid models generated was evaluated through correlation and error parameters, along with the number of iterations required to achieve the lowest error rate. Upon comprehensive review, the SVR-SSA hybrid model emerged as the most promising, exhibiting an R2 value of 0.997293, a Root Mean Square Error (RMSE) of 0.2906, and a Normalized Mean Square Error (NMSE) of 5×10-4 at the thousandth iteration in the train dataset. These values for the test data set are calculated as 0.999754, 0.1625, and 2×10-4, respectively. Hence, it is deemed the most optimum model for this research endeavour.

Advances in Engineering and Intelligence Systems, 2024

Voltage source inverters (VSIs) are the most common type of inverter used in power electronic sys... more Voltage source inverters (VSIs) are the most common type of inverter used in power electronic systems for distributed generation sources. In three-phase systems, using three-phase conversion to the synchronous reference frame, linear controllers can be easily used to control the inverter due to the conversion of AC parameters in the synchronous frame to dc components. The design of linear controllers in single-phase systems is often difficult because of the oscillating nature of these systems. In some cases, single-phase conversion to synchronous frame is used, which has its problems, such as delays in converting single-phase values to two-phase. To tackle this problem, proportional-resonance (PR) instruments can be used at a specific resonant frequency. However, for single-phase systems where all the harmonies are present in the inverter current, an unlimited number of PR instruments are required. Therefore, not all harmonies can be controlled in this way. To overcome this, a iterative control system can be used. The control method presented in this paper uses delay intervals with positive feedback. Under these conditions, the control gain in the dynamic model of the iterative control system is very high for all harmonies and allows tracking the reference current and voltage values with a zero steady-state error in all harmonies. In addition, the performance of the iterative control system in the face of single-phase systems will be very favorable.

Advances in Engineering and Intelligence Systems, 2024

Dynamic management of virtual machines (VMs) in cloud data centers is essential for achieving fle... more Dynamic management of virtual machines (VMs) in cloud data centers is essential for achieving flexibility, efficiency, and high productivity in cloud systems. With the rapid growth of cloud technologies and increasing demand for cloud-based services, effective resource management has become a critical factor for ensuring quality service delivery while minimizing operational costs. This is particularly important for organizations seeking to optimize their resource utilization and adapt dynamically to fluctuating workloads. This paper introduces a novel model designed to address these challenges by enhancing existing algorithms and employing advanced techniques. The approach integrates genetic algorithms and refrigeration simulation into the migration replacement process, leveraging absorbing Markov chains for predictive analysis. By continuously monitoring resource status, analyzing incoming data, and forecasting critical server conditions, the model effectively reduces unnecessary VM migrations. Simulations conducted using the Clodsim environment demonstrate the model’s efficiency in reducing energy consumption across low, medium, and high-load scenarios. The proposed method achieves an average reduction in energy consumption of 17% compared to state-of-the-art methods, while also minimizing violations of service-level agreements (SLA). This research highlights the importance of combining predictive analytics with robust optimization techniques to improve cloud resource management. By achieving significant energy savings and enhancing system reliability, the proposed model offers a practical, sustainable framework for dynamic VM management, addressing the challenges posed by growing user demands and resource constraints in modern cloud data centers.

Advances in Engineering and Intelligence Systems, 2024

Designing or appraising framed concrete buildings exposed to high eccentric loads requires precis... more Designing or appraising framed concrete buildings exposed to high eccentric loads requires precise reinforced concrete (RC) torsional strength estimates. Unfortunately, semi-empirical formulae still fail to adequately predict the torsional capacity of RC beams, particularly over-reinforced and strong ones. To overcome this limitation, accurate Machine Learning (ML) models might replace more sophisticated and computationally intensive models. This work evaluates and determines the most effective tree-based machine learning algorithms to estimate the torsional capacity (T_r) of RC beams subjected to pure torsion. The objective of the present work is to provide innovative hybrid models that combine the concepts of the Adaptive neuro fuzzy inference systems (ANFIS) model with other optimization approaches, such as the Giant trevally algorithm (GTA) and Honey badger algorithm (HBA), to accurately forecast the T_r. A total of 202 RC rays were collected to form the data set. To facilitate the application of the ANFIS models, a training set and a testing set were created from the database. Out of the 202 samples in the database, 25 percent (51) were used for evaluation and 75 percent (151) were used for learning. ANF-GTA demonstrated superior performance compared to ANF-HBA, with a 50% higher performance in the learning phase and an 80% higher performance in the evaluation stage, as measured by the MedSE index values. The ANF-GTA obtained lower SMAPE index values of 5.2192 and 5.39 compared to the values of 8.0622 and 9.3783 obtained by the ANF-HBA during the learning and assessment phases, respectively.

Advances in Engineering and Intelligence Systems, 2024

The rapid evolution of healthcare services has been driven by the convergence of the Internet of ... more The rapid evolution of healthcare services has been driven by the convergence of the Internet of Things and cloud computing technologies, inherently transforming telemedicine. This study evaluates the transformative effect of IoT and cloud-based solutions through the telemedicine domain, especially in the remote delivery of healthcare services and medical consultations. It explores how IoT-enabled medical devices facilitate continuous remote patient monitoring and real-time health data collection and emphasizes the role of wearable devices comprising smartwatches and fitness trackers in elevating data accuracy and offering personalized health insights. Cloud computing's key role in telemedicine is investigated through its scalable data storage and processing capabilities, which manage the vast amounts of data generated by IoT devices. The study also discusses the benefits of cloud-based telemedicine platforms, including scalability, accessibility, cost-effectiveness, and the implementation of security measures to address data privacy concerns. The combination of IoT and cloud methods indicates various advantages, such as improving patient outcomes, expanding healthcare accessibility, and enabling data-driven decision-making for medical professionals. Real-world case studies outline successful implementations of these methods, which result in decreased hospital readmissions, improved patient engagement in health management, and centralized critical care expertise. However, significant challenges are addressed, including data security, interoperability, and reliable internet connectivity in various healthcare settings. The reports highlight the necessity of more studies on the integration of artificial intelligence in telemedicine and the adoption of edge computing solutions to overcome cloud-related issues in low-connectivity scenarios. This study provides valuable insights into redefining telemedicine in the digital era and serves as a guide for future developments in healthcare technology.

Advances in Engineering and Intelligence Systems, 2025

Advances in Engineering and Intelligence Systems, 2024