Variety Of Performance Metrics Research Articles

HOEEACR: Hybrid Optimized Energy-Efficient Adaptive Clustered Routing for WSN

Wireless Sensor Networks have been used for sensing and gathering data about an environment from a remote location for many years in a variety of engineering applications. In WSN, nodes must overcome energy consumption to function efficiently. To resolve these issues and extend the usefulness of the network, clustering and routing algorithms are promising. One of the main issues with WSNs is that they lack restricted energy sources. To overcome this issue, a Hybrid Optimized Energy-Efficient Adaptive Clustered Routing approach (HOEEACR) has been proposed for WSNs. This paper presents the Genetic Bee Colony (GBC) Algorithm for Cluster Head Selection by considering distances to neighbors, residual energy, node degrees, and node centralities. Furthermore, an optimal routing path for the cluster heads is found by utilizing the Aquila with African Vulture Optimization (AAVO) algorithm. The AAVO optimizes network performance using residual energy, node degree, and distance. The proposed HOEEACR method has been extensively tested to ensure network lifetime and energy efficiency. According to experimental results, the proposed HOEEACR method consistently outperforms existing techniques on a variety of performance metrics.

New unfreezing strategy of transfer learning in satellite imagery for mapping the diversity of slum areas: A case study in Kenitra city—Morocco

The purpose behind this paper research is to localize and detect, for the first time, three types of shantytowns that can exist in both urban and rural areas in Morocco. The reason why we have conducted this study is the absence of diversity slum mapping in the form of dynamic GIS data, which can be a part of national development strategies to tackle the risk of further proliferation of shantytowns. Therefore, we first create a new database integration from five countries whose content contains 1366 high-resolution patches of 224 × 224 pixels divided into five classes: two types of “Urban Slum”, one type of “Rural Slum”, “Formal Buildings”, and non-built-up area. Second, we suggest employing a variety of performance metrics to come up with the best transfer learning-based models for the unfreezing stage. The study reveals that by handling the unfreezing of the front layers, we can produce a reliable new model where one condition is fulfilled, namely, where high-frequency dissimilarities have arisen between the target and the source datasets.Quantitative results of these methodologies show that our dataset and our unfreezing strategy for the mobilenetv2 model are more accurate compared to other competing models. This new model can localize all possible changes in morphology and shape among the three-slum types with state-of-the-art performance that reaches an overall accuracy of 98.17%, a loss of 0.062, and a kappa score of 97.71%. Our dataset and the code will be publicly available at the github11https://github.com/mouddentarik/New_Unfreezing_Strategy. to share our results.

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

SummaryAn attack on VANET known as a Sybil attack involves the attacker claiming or stealing multiple identities with the intent of causing havoc on the network's operation by disseminating false information. To protect the VANET network from Sybil attack, a variety of methods have been proposed. In order to compensate for the Sybil attack on VANETs, a rumor riding with salp swarm optimization (SSO) based Routing protocol is developed in this research work. The primary goal of this investigation is to discover how VANETs are vulnerable to the Sybil attack in order to improve their security. Rumor riding will be used to identify the Sybil attack in the suggested strategy. VANETs are protected by the Blowfish Algorithm, which uses SSO to select the best key for each network. The collective intelligence and dynamic adaptation of SALP swarm optimization algorithm enable efficient identification of rumor sources in complex networks. The algorithm is scalable, flexible, and adept at reducing false positives and negatives, making it suitable for large‐scale monitoring. Its parallel processing capabilities and robustness ensure rapid and reliable detection. Using the NS2 platform, the proposed method is compared to existing benchmark in terms of delay, packet delivery ratio (PDR), packet loss, throughput, and attack detection rate. The proposed SSO has reduced the packet loss by nearly 5% and delay by nearly 7%, when compared with existing techniques. The delivery ratio of packet for proposed model is increased up to 8% of PDR, when compared with ACO, PSO, and FA. According to this evidence, it is clear that the proposed SSO outperformed existing approaches in a variety of performance metrics.

Designing a computer-vision-based artifact for automated quality control: a case study in the food industry

AbstractReducing waste through automated quality control (AQC) has both positive economical and ecological effects. In order to incorporate AQC in packaging, multiple quality factor types (visual, informational, etc.) of a packaged artifact need to be evaluated. Thus, this work proposes an end-to-end quality control framework evaluating multiple quality control factors of packaged artifacts (visual, informational, etc.) to enable future industrial and scientific use cases. The framework includes an AQC architecture blueprint as well as a computer vision-based model training pipeline. The framework is designed generically, and then implemented based on a real use case from the packaging industry. As an innovate approach to quality control solution development, the data-centric artificial-intelligence (DCAI) paradigm is incorporated in the framework. The implemented use case solution is finally tested on actual data. As a result, it is shown that the framework’s implementation through a real industry use case works seamlessly and achieves superior results. The majority of packaged artifacts are correctly classified with rapid prediction speed. Deep-learning-based and traditional computer vision approaches are both integrated and benchmarked against each other. Through the measurement of a variety of performance metrics, valuable insights and key learnings for future adoptions of the framework are derived.

Enhancing investment performance of Black-Litterman model with AI hybrid system: Can it be done?

The B-L (Black-Litterman) model combines the subjective views of investors with the market's historical equilibrium return through the Bayesian theorem. This approach somewhat compensates for the limitations of the Mean-Variance model, making the asset allocation model attract significant attention. However, due to many investors lacking ample investment experience, they might struggle to provide appropriate subjective views. To address this issue, an Iaah (Industry Asset Allocation Hybrid) system based on the B-L model is proposed. First, a combined model is constructed, which simulates view return vectors and solves the problem of inaccurate view quantification. Then, the Iaah system introduces a dynamic condition correlation algorithm to optimize inherent parameters and explores asset allocation results in the time-varying effects. Next, it expands the types of objective functions and improves the deficiency of only considering the return and variance while ignoring the downside risk. Finally, based on a variety of performance metrics, the Iaah system is evaluated from multiple perspectives to test its practical value. The experiment results with the daily data of 10 SSE (Shanghai Stock Exchange) industry indexes show that the proposed system not only realizes the diversified allocation of industry assets and enhances the investment performance, but also exhibits strong robustness against changes in subjective parameters. It is conducive to managing personal wealth and achieving preservation and appreciation.

FPGA-Based PUF Designs: A Comprehensive Review and Comparative Analysis

Field-programmable gate arrays (FPGAs) have firmly established themselves as dynamic platforms for the implementation of physical unclonable functions (PUFs). Their intrinsic reconfigurability and profound implications for enhancing hardware security make them an invaluable asset in this realm. This groundbreaking study not only dives deep into the universe of FPGA-based PUF designs but also offers a comprehensive overview coupled with a discerning comparative analysis. PUFs are the bedrock of device authentication and key generation and the fortification of secure cryptographic protocols. Unleashing the potential of FPGA technology expands the horizons of PUF integration across diverse hardware systems. We set out to understand the fundamental ideas behind PUF and how crucially important it is to current security paradigms. Different FPGA-based PUF solutions, including static, dynamic, and hybrid systems, are closely examined. Each design paradigm is painstakingly examined to reveal its special qualities, functional nuances, and weaknesses. We closely assess a variety of performance metrics, including those related to distinctiveness, reliability, and resilience against hostile threats. We compare various FPGA-based PUF systems against one another to expose their unique advantages and disadvantages. This study provides system designers and security professionals with the crucial information they need to choose the best PUF design for their particular applications. Our paper provides a comprehensive view of the functionality, security capabilities, and prospective applications of FPGA-based PUF systems. The depth of knowledge gained from this research advances the field of hardware security, enabling security practitioners, researchers, and designers to make wise decisions when deciding on and implementing FPGA-based PUF solutions.

A Novel Hybrid Optimization With Ensemble Constraint Handling Approach for the Optimal Materialized Views

The datawarehouse is extremely challenging to work with, as doing so necessitates a significant investment of both time and space. As a result, it is essential to enable rapid data processing in order to cut down on the amount of time needed to respond to queries that are sent to the warehouse. To effectively solve this problem, one of the significant approaches that should be taken is to take the view of materialization. It is extremely unlikely that all of the views that can be derived from the data will ever be materialized. As a result, view subsets need to be selected intelligently in order to enable rapid data processing for queries coming from a variety of locations. The Materialized view selection problem is addressed by the model that has been proposed. The model is based on the ensemble constraint handling techniques (ECHT). In order to optimize the problem, we must take into account the constraints, which include the self-adaptive penalty, the Epsilon ()-parameter, and the stochastic ranking. For the purpose of making a quicker and more accurate selection of queries from the data warehouse, the proposed model includes the implementation of an innovative algorithm known as the constrained hybrid Ebola with COATI optimization (CHECO) algorithm. For the purpose of computing the best possible fitness, the goals of "processing cost of the query," "response cost," and "maintenance cost" are each defined. The top views are selected by the CHECO algorithm based on whether or not the defined fitness requirements are met. In the final step of the process, the proposed model is compared to the models already in use in order to validate the performance improvement in terms of a variety of performance metrics.

Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins

Abstract. The calibration of macroscale hydrological models is often challenged by the lack of adequate observations of river discharge and infrastructure operations. This modeling backdrop creates a number of potential pitfalls for model calibration, potentially affecting the reliability of hydrological models. Here, we introduce a novel numerical framework conceived to explore and overcome these pitfalls. Our framework consists of VIC-Res (a macroscale model setup for the Upper Mekong Basin), which is a novel variant of the Variable Infiltration Capacity (VIC) model that includes a module for representing reservoir operations, and a hydraulic model used to infer discharge time series from satellite data. Using these two models and global sensitivity analysis, we show the existence of a strong relationship between the parameterization of the hydraulic model and the performance of VIC-Res – a codependence that emerges for a variety of performance metrics that we considered. Using the results provided by the sensitivity analysis, we propose an approach for breaking this codependence and informing the hydrological model calibration, which we finally carry out with the aid of a multi-objective optimization algorithm. The approach used in this study could integrate multiple remotely sensed observations and is transferable to other poorly gauged and heavily regulated river basins.

XGBoost and Random Forest Algorithms: An in Depth Analysis

Machine learning is playing an increasingly important role in many facets of our lives as technology develops, including forecasting weather, figuring out social media trends, and predicting prices on the world market. This significance invoked the demand of some efficient predicting models that can easily handle complex data and provide maximum accurate results. XGBoost and Random Forest are upgradable ensemble techniques used to solve regression and classification problems that have evolved and proved to be dependable and reliable machine learning challenge solvers. In this research paper, we undertake a comprehensive analysis and comparison of these two prominent machine learning algorithms. The first half of the research includes relevant overview on both of the techniques, significance and evolution of both algorithms. The latter part of this study involves a meticulous comparative analysis between Random Forest and XGBoost, scrutinizing facets such as time complexity, precision, and reliability. We examine their distinctive approaches to handling regression and classification problems while closely examining their subtle handling of training and testing datasets. A thorough quantitative evaluation using a variety of performance metrics, such as the F1-score, Recall, Precision, Mean Squared Erro

OCI-OLSR: An Optimized Control Interval-Optimized Link State Routing-Based Efficient Routing Mechanism for Ad-Hoc Networks

MANET (Mobile Ad hoc Networks) functionality is determined by routing protocols’ ability to adjust to atypical changes in information and communication technologies, topological systems, and connection status. Due to interference, node migration, the growth of several pathways, security, and propagation loss, MANET network configurations are dynamic. The proactive routing protocol enhances the message flow utilized in the neighborhood discovery process by using the multipoint relays (MPR) approach. In order to increase the protocol’s effectiveness and efficiency while maintaining the OLSR protocol’s reliability, the research presented in this paper proposed an improved OCI-OLSR (Optimized Control Interval-Optimized Link State Routing) that focuses on better control interval management, an advanced MPR selection process, reducing neighbor hold time as well as decreasing flooding. The suggested proposed protocol was examined using the NS3 simulator, and it was compared to the standard OLSR version and AODV(Ad-hoc On-Demand Routing) routing protocol. According to the analysis’s findings, the suggested system has a lot of promise in terms of a variety of performance metrics under diverse conditions. Overall, the article makes the case that the OCI-OLSR protocol may enhance the performance of the regular OLSR protocol in wireless ad hoc networks by addressing a number of the protocol’s flaws.

Graph based fuzzy clustering algorithm for crime report labelling

Globalization influences the frequency and severity of localized traditional patterns of crime in addition to creating conditions for new types of crime. The scope and makeup of traditional crime have been significantly impacted by the world’s compression. This makes it impossible for law enforcement officials to examine crime reports for the purpose of conducting investigations and taking the necessary preventive measures. Clustering facilitates investigations by grouping crime reports into different categories based on the crime types. In the proposed work, the crime reports are preprocessed and embedded with the help of the BERT model to capture the contextual meaning of the reports. An undirected graph is constructed considering each crime report vector as a node where an edge exists between a pair of nodes only if the cosine similarity between them is more than a threshold value. The constructed graph is partitioned based on the concepts of node betweenness, edge connectivity, and two proposed concepts, namely, cut of a node, and safe node, introduced in the paper. Based on the edge connectivity of the graph, we decide whether the graph needs to be further partitioned or not. If edge connectivity is high, we consider the graph as a cluster; otherwise, we partition the graph with the help of a node, say v, with the highest node betweenness. During partition, we find the cut of v to determine whether v is safe or not. If v is safe, the algorithm provides two overlapping subgraphs of the graph; on the other hand, if it is not safe, then the algorithm provides a modified graph with a replica of node v. The algorithm is iterative in nature and terminates when all subgraphs are of higher edge connectivity. As the generated subgraphs are overlapped in nature, a novel graph theory-based fuzzification technique is utilized to measure the membership value of each node in different subgraphs, which allows investigators to focus on the most serious crimes in the reports. The proposed method is assessed using crime report datasets as well as other text datasets and compared with several state-of-the-art methods by utilizing a variety of performance metrics to reflect the effectiveness of the method in different domains.

An Energy-Efficient Protocol based on Recursive Geographic Forwarding Mechanisms for Improving Routing Performance in WSN

In situations where a regular network may not be appropriate or is unable to function correctly, data collection and real-time transmission are commonly done using Wireless Sensor Networks (WSNs). In order to accomplish this type of operation, the network's durability and performance are extremely vital. One of the key elements that directly affect a network's lifespan and performance is the routing protocol. This study proposes a Geographic Forwarding Energy Efficient Routing Protocol (GF-EERP), an enhanced version of the Geographic Energy Aware Routing (GEAR) protocol that helps to extend the network's lifespan and performance. The introduction of node categorization, a unique technique for choosing the region head, use of multi-hop communication method and removal of dead nodes serve as the foundation of the GF-EERP. In this study, the proposed protocol's performance is also compared to other protocols already in use, like Geographic Adaptive Fidelity (GAF), Geographic Energy Aware Routing (GEAR), Greedy Perimeter Stateless Routing (GPSR) and Multihop-Geographic Energy Aware Routing (M-GEAR), using a variety of performance metrics including Network Delay, Data Delivery Ratio and Network Throughput. The simulation outcomes prove that the performance of GF-EERP, with its distinct node categorization and region head selection mechanism is superior to the existing protocols.

Performance Evaluation of Machine Learning Algorithm in Various Datasets

Machine learning is one of the fast-growing areas of computer science, with far-reaching applications. There are several applications for machine learning. The most significant of which is supervised learning. Supervised learning is common in classification problems. In this study, frequently used twelve machine learning algorithms are considered: NB, LDA, LR, ANN, SVM, K-NN, HT, DT, C4.5, CART, RF and BB. We apply these algorithms on seven datasets. The main goal of this study was to evaluate the performance of the machine learning algorithms on both binary and multiple classification problems using a variety of performance metrics: accuracy, kappa statistic, precision, recall, specificity, F-measure, MAE, RMSE and MCC. Here, we found that RF algorithm proved to have the best performance in three out of seven datasets. But the other four algorithms: NN, NB, BB and LR also performed well.

Feature‐driven robust surgery scheduling

AbstractPatient features such as gender, age, and underlying disease are crucial to improving the model fidelity of surgery duration. In this paper, we study a robust surgery scheduling problem augmented by patient feature segmentation. We focus on the surgery‐to‐operating room allocations for elective patients and future emergencies. Using feature data, we classify patients into different types using machine learning methods and characterize the uncertain surgery duration via a feature‐based cluster‐wise ambiguity set. We propose a feature‐driven adaptive robust optimization model that minimizes an overtime riskiness index, which helps mitigate both the magnitude and probability of working overtime. The model can be reformulated as a second‐order conic programming problem. From the reformulation, we find that minimizing the overtime riskiness index is equivalent to minimizing a Fano factor. This makes our robust optimization model easily interpretable to healthcare practitioners. To efficiently solve the problem, we develop a branch‐and‐cut algorithm and introduce symmetry‐breaking constraints. Numerical experiments demonstrate that our model outperforms benchmark models in a variety of performance metrics.

A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions.

Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain's requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario's requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios' nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism's efficiency.

Asymmetric U-Shaped-Gated TFET for Low-Power Ana–Digi Applications at Sub-7-nm Technology Node: A Simulation-Based Optimization Study

In this article, for the first time, an asymmetric U-shaped-gated tunnel FET (AU-TFET), with a unique vertical channel epilayer, at sub-7-nm technology node, has been proposed and investigated for its suitability to be a universal device. After validating the simulation scheme with the experimental results of fabricated TFET devices, the impact of thickness of the said epilayer ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}_{\text {epi}}$ </tex-math></inline-formula> ), on device performance, has been thoroughly investigated in terms of a variety of performance metrics, both in analog and digital (Ana–Digi) domains. To increase the vitality of the work, the device-level analysis is stretched to the circuit level. The impact on the inverter performance, both in Ana–Digi domains, in terms of fundamental circuit performance parameters, viz., dc gain, short-circuit power dissipation during switching, noise margin (NM), and so on, has been studied, and ultimately, the most optimized TFET structure, in each domain, has been identified. Finally, in this whole device/circuit co-analysis, after summing up all the performance metrics in both the domains while looking for meeting the low-power (LP) requirements (following the goals, as applicable, of international roadmaps), altogether, we have found that AU-TFET with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}_{\text {epi}} $ </tex-math></inline-formula> = 6 nm could be considered as the ultimate optimized universal LP Ana–Digi TFET structure.

High-dimensional normalized data profiles for testing derivative-free optimization algorithms.

This article provides a new tool for examining the efficiency and robustness of derivative-free optimization algorithms based on high-dimensional normalized data profiles that test a variety of performance metrics. Unlike the traditional data profiles that examine a single dimension, the proposed data profiles require several dimensions in order to analyze the relative performance of different optimization solutions. To design a use case, we utilize five sequences (solvers) of trigonometric simplex designs that extract different features of non-isometric reflections, as an example to show how various metrics (dimensions) are essential to provide a comprehensive evaluation about a particular solver relative to others. In addition, each designed sequence can rotate the starting simplex through an angle to designate the direction of the simplex. This type of features extraction is applied to each sequence of the triangular simplexes to determine a global minimum for a mathematical problem. To allocate an optimal sequence of trigonometric simplex designs, a linear model is used with the proposed data profiles to examine the convergence rate of the five simplexes. Furthermore, we compare the proposed five simplexes to an optimized version of the Nelder–Mead algorithm known as the Genetic Nelder–Mead algorithm. The experimental results demonstrate that the proposed data profiles lead to a better examination of the reliability and robustness for the considered solvers from a more comprehensive perspective than the existing data profiles. Finally, the high-dimensional data profiles reveal that the proposed solvers outperform the genetic solvers for all accuracy tests.

Forecasting fine-grained city-scale cellular traffic with sparse crowdsourced measurements

With the rapid development of wireless technology and the edge computing applications, an increasing number of 4G/5G infrastructure are densely deployed to meet the booming cellular traffic demands. Monitoring and forecasting urban cellular traffic is fundamental for urban planning, network resources allocation, traffic engineering, etc. In this paper, we address the crowdsourcing-based urban cellular traffic prediction problem, i.e., to predict the city-scale fine-grained cellular traffic patterns based on partial user-generated measurements. We propose a novel deep generative adversarial network (GAN) model called CrowdGAN to solve the problem. Specifically, CrowdGAN employs a convolutional Long Short-Term Memory (LSTM) network to extract spatio-temporal features from sparse traffic maps, and adopts a novel design of co-training a generator and a discriminator under the supervision of an accuracy assurance network to generate a high-resolution cellular traffic map for prediction. We implement the proposed CrowdGAN in TensorFlow and evaluate its performance using two real-world cellular traffic datasets. Extensive experiments show that CrowdGAN significantly outperforms the baselines on a variety of performance metrics, and achieves at least 47% reduction in root-mean-squared error compared to the state-of-the-art.

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

AbstractDecision making under deep uncertainty (DMDU) approaches are well‐suited for making decisions about infrastructure to manage flooding exacerbated by climate change. One important system for climate resilience and flood management is green infrastructure, which refers to a network of natural and semi‐engineered areas that provides ecosystem functions. Green infrastructure is often characterized as a low‐regret strategy with multiple co‐benefits under uncertainty. These attributes enable green infrastructure to be an important adaptation strategy under DMDU frameworks for flood management. However, DMDU analyses that include green infrastructure are still relatively limited, perhaps due to computational or modeling complexity and other barriers. This paper identifies and reviews publications in the flood management literature that use DMDU frameworks and refer to green infrastructure adaptation strategies, in order to identify trends and inform future research. The reviewed publications are categorized according to a variety of performance metrics, climate change scenarios, DMDU metrics, and hydrologic modeling techniques, and represent several adaptation strategies applied to case studies on five continents using a range of data sources and assumptions. This paper highlights a number of solutions that can be employed to facilitate additional research at the intersection of these fields. Primary among these is the transparent documentation and use of open source models, methods, and data. Future research should also focus on communication among different stakeholders, particularly in ensuring definitions, assumptions, and data requirements are clear. These partnerships can facilitate effective application of robust strategies such as green infrastructure for urban adaptation to the effects of climate change.

Sustainable Technical Debt-Aware Computing Model for Virtual Machine Migration (TD4VM) in IaaS Cloud

In the cloud, optimal CPU and memory utilization can lead to low energy consumption, which is an important aspect of green computing. However, constantly changing workloads may contribute to resource over- or underutilization. The former violates the service level agreement’s quality of service constraints. The latter indicates that as workload decreases, virtual machine resource utilization decreases. They introduce difficult decision-making tasks when dynamically adapting (e.g., migrating) a virtual machine in order to maximize its resource utilization over time. To address these challenges, we propose a newer mathematical model called the technical debt-aware computing model for virtual machine migration (TD4VM). The model promotes a holistic approach to dynamic virtual machine adaptation for cloud service providers and addresses existing issues regarding logical aspects of virtual machine adaptation in a highly dynamic cloud environment, which includes a measurement mechanism and estimation guidelines for estimating future debt and utility. Technical debt-aware models make decisions based on VM operating costs, quality, minimizing SLA violations, and incurring technical debt. This approach connects decisions about virtual machine migration that affect overall utility over time. Our method can determine whether a virtual machine should be moved when it is over or underutilized based on its technical debt. The experimental results on a dataset obtained from the Materna-trace-1 demonstrate that the proposed approach outperforms other state-of-the-art methods on a variety of performance metrics. A numerical comparison shows that TD4VM outperforms the other approaches, with VM resource economies of 171.84%, 91.33%, 97.85%, and 93.89% for TD4VM, LRMMT, IQRMC, and IQRMMT, respectively. Additionally, we quantify the debt amassed using TD4VM and state-of-the-art techniques. When compared to LRMMT, IQRMC, and IQRMMT, which cost (in $) 0.77, 0.73, and 0.76, respectively, TD4VM accumulates the minimum debt of 0.17.