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The widespread Internet of Things (IoT) technologies in day life indoor environments result in an enormous amount of daily generated data, which require reliable data analysis techniques to enable efficient exploitation of this data. The recent developments in deep learning (DL) have facilitated the processing and learning from the massive IoT data and learn essential features swiftly and professionally for a variety of IoT applications on smart indoor environments. This study surveys the recent literature on exploiting DL for different indoor IoT applications. We aim to give insights into how the DL approaches can be employed from various viewpoints to develop improved Indoor IoT applications in two distinct domains: indoor positioning/tracking and activity recognition. A primary target is to effortlessly amalgamate the two disciplines of IoT and DL, resultant in a broad range of innovative strategies in indoor IoT applications, such as health monitoring, smart home control, robotics, etc. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three beforementioned domains. Eventually, we proposed and discussed a set of matters, challenges, and some new directions in incorporating DL to improve the efficiency of indoor IoT applications, encouraging and stimulating additional advances in this auspicious research area. [ABSTRACT FROM AUTHOR]

The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization problem that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this problem due to their potential to approximate the optimal solution for several complicated optimization problems. Despite that, the existing metaheuristic algorithms still suffer from sluggish convergence rates and stagnation in local optima when applied to tackle this problem. Therefore, this study presents a new parameter estimation technique, namely HKOA, based on integrating the recently published Kepler optimization algorithm (KOA) with the ranking-based update and exploitation improvement mechanisms to accurately estimate the unknown parameters of the third-, single-, and double-diode models. The former mechanism aims at promoting the KOA's exploration operator to diminish getting stuck in local optima, while the latter mechanism is used to strengthen its exploitation operator to faster converge to the approximate solution. Both KOA and HKOA are validated using the RTC France solar cell and five PV modules, including Photowatt-PWP201, Ultra 85-P, Ultra 85-P, STP6-120/36, and STM6-40/36, to show their efficiency and stability. In addition, they are extensively compared to several optimization techniques to show their effectiveness. According to the experimental findings, HKOA is a strong alternative method for estimating the unknown parameters of PV models because it can yield substantially different and superior findings for the third-, single-, and double-diode models. [ABSTRACT FROM AUTHOR]

Uncertainty is an omnipresent aspect of real-world data, necessitating innovative approaches for representation learning. In this paper, we introduce an avant-garde model, namely, the neutrosophic-based transformer network (NTN), which leverages the fusion of neutrosophic logic and transformer architecture to address the multifaceted challenge of modeling and managing uncertainties. The design of NTN includes three primary building blocks, namely, neutrosophic encoding, multipath network, and fusion and decision modules. The neutrosophic encoding module applies a convolving window to map image data into the neutrosophic domain, with three subsets, namely, truth, indeterminacy, and falsehood, which model the inherent uncertainties in pixel attributes. Then, three network paths are built with a transformer encoder to extract rich and adaptive representations from the generated neutrosophic data, making up the multipath network module. Finally, the fusion and decision modules combine diverse knowledge from the paths, enabling comprehensive representation learning. Extensive experimentations on Fashion-MNIST and CIFAR-10 dataset validate the effectiveness and efficiency of NTN, outperforming the cutting-edge vision models under different levels of uncertainties. The NTN paves the way for a new era of representation learning, where uncertainties are harnessed as a valuable resource instead of an impediment, promising broad applicability in real-world settings, in which data are intrinsically uncertain. [ABSTRACT FROM AUTHOR]

In the aftermath of the COVID-19 pandemic, the need for efficient and reliable disease diagnosis in smart cities has become increasingly serious. In this study, we introduce a novel blockchain-based federated learning framework tailored specifically for the diagnosis of pandemic diseases in smart cities, called BFLPD, with a focus on COVID-19 as a case study. The proposed BFLPD takes advantage of the decentralized nature of blockchain technology to design collaborative intelligence for automated diagnosis without violating trustworthiness metrics, such as privacy, security, and data sharing, which are encountered in healthcare systems of smart cities. Cheon–Kim–Kim–Song (CKKS) encryption is intelligently redesigned in BFLPD to ensure the secure sharing of learning updates during the training process. The proposed BFLPD presents a decentralized secure aggregation method that safeguards the integrity of the global model against adversarial attacks, thereby improving the overall efficiency and trustworthiness of our system. Extensive experiments and evaluations using a case study of COVID-19 ultrasound data demonstrate that BFLPD can reliably improve diagnostic accuracy while preserving data privacy, making it a promising tool with which smart cities can enhance their pandemic disease diagnosis capabilities. [ABSTRACT FROM AUTHOR]

Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new obstacle or hazard is recognized or if the target is moved during the mission. In brief, the planning of UAV routes might optimize a specific problem determined by the application, such as the moving target problem (MTP), flight time and threats, or multiobjective navigation. The complexity of MTP ranges from NP-hard to NEXP-complete because there are so many probabilistic variables involved. Therefore, it is hard to detect a high-quality solution for this problem using traditional techniques such as differential calculus. Therefore, this paper hybridizes differential evolution (DE) with two newly proposed updating schemes to present a new evolution-based technique named hybrid differential evolution (HDE) for accurately tackling the MTP in a reasonable amount of time. Using Bayesian theory, the MTP can be transformed into an optimization problem by employing the target detection probability as the fitness function. The proposed HDE encodes the search trajectory as a sequence of UAV motion pathways that evolve with increasing the current iteration for finding the near-optimal solution, which could maximize this fitness function. The HDE is extensively compared to the classical DE and several rival optimizers in terms of several performance metrics across four different scenarios with varying degrees of difficulty. This comparison demonstrates the proposal's superiority in terms of the majority of used performance metrics. [ABSTRACT FROM AUTHOR]

COVID-19-like pandemics are a major threat to the global health system have the potential to cause high mortality across age groups. The advance of the Internet of Medical Things (IoMT) technologies paves the way toward developing reliable solutions to combat these pandemics. Medical images (i.e., X-rays, computed tomography (CT)) provide an efficient tool for disease detection and diagnosis. The cost, time, and efforts for acquiring and annotating, for instance, large CT datasets make it complicated to obtain large numbers of samples from a single institution. However, owing to the necessity to preserve the privacy of patient data, it is challenging to build a centralized dataset from many institutions, especially during a pandemic. Moreover, heterogeneity between institutions presents a barrier to building efficient screening solutions. Thus, this paper presents a fog-based federated generative domain adaption framework (FGDA), where fog nodes aggregate patients' data necessary to collaboratively train local deep-learning models for disease screening in medical images from different institutions. Local differential privacy is presented to protect the local gradients against attackers during the global model aggregation. In FGDA, the generative domain adaptation (DA) method is introduced to handle data discrepancies. Experimental evaluation on a case study of COVID-19 segmentation demonstrated the efficiency of FGDA over competing learning approaches with statistical significance. [ABSTRACT FROM AUTHOR]

End-of-life vehicle (ELV) management is currently one of the most pressing environmental issues. Waste must be recycled to ensure that ELV management is both environmentally and economically sustainable. Cairo boasts the greatest percentage of Egyptian citizens who own a car and the country's oldest automotive fleet. There is a great desire to open a new ELV recycling factory in this city. One of the most difficult strategic issues facing decision-makers is determining where to build new facilities. ELV recycling facility selection is a multi-criteria and highly uncertain challenge, and this research presents a novel approach to help with the process. An extension of the Additive Ratio Assessment (ARAS) approach in a neutrosophic type-2 environment ((N-type-2 ARAS)) is described for the first time. The new approach (N-type-2 ARAS) is used to choose an ELV recycling facility location. The proposed N-type-2 ARAS method's potential and applicability are illustrated in the context of a real-life case study in Egypt. This study used five main criteria, twenty sub-criteria, and four alternatives (locations) to select the best location. The results show that Luxor is the best location. This method has been proven reliable and consistent compared to the current state-of-the-art interval type-2 fuzzy set-based MCDM methods. This study's sensitivity analysis was conducted to show rank stability from the (N-type-2 ARAS) methodology. [ABSTRACT FROM AUTHOR]

Lung ultrasound images have shown great promise to be an operative point-of-care test for the diagnosis of COVID-19 because of the ease of procedure with negligible individual protection equipment, together with relaxed disinfection. Deep learning (DL) is a robust tool for modeling infection patterns from medical images; however, the existing COVID-19 detection models are complex and thereby are hard to deploy in frequently used mobile platforms in point-of-care testing. Moreover, most of the COVID-19 detection models in the existing literature on DL are implemented as a black box, hence, they are hard to be interpreted or trusted by the healthcare community. This paper presents a novel interpretable DL framework discriminating COVID-19 infection from other cases of pneumonia and normal cases using ultrasound data of patients. In the proposed framework, novel transformer modules are introduced to model the pathological information from ultrasound frames using an improved window-based multi-head self-attention layer. A convolutional patching module is introduced to transform input frames into latent space rather than partitioning input into patches. A weighted pooling module is presented to score the embeddings of the disease representations obtained from the transformer modules to attend to information that is most valuable for the screening decision. Experimental analysis of the public three-class lung ultrasound dataset (PCUS dataset) demonstrates the discriminative power (Accuracy: 93.4%, F1-score: 93.1%, AUC: 97.5%) of the proposed solution overcoming the competing approaches while maintaining low complexity. The proposed model obtained very promising results in comparison with the rival models. More importantly, it gives explainable outputs therefore, it can serve as a candidate tool for empowering the sustainable diagnosis of COVID-19-like diseases in smart healthcare. [ABSTRACT FROM AUTHOR]

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched. [ABSTRACT FROM AUTHOR]

This paper introduces a novel physical-inspired metaheuristic algorithm called "Light Spectrum Optimizer (LSO)" for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm. [ABSTRACT FROM AUTHOR]

There are several multicriteria decision-making (MCDM) approaches presented in the literature with their characteristics. Although traditional MCDM approaches are considered a proper implementation to select the best alternative from available types, they failed to consider uncertainty which is quite high and desires to be thoughtfully measured in the selection process. This research focuses on extending MCDM in the neutrosophic environment using axiomatic design (AD) as a novel contribution to selecting appropriate Computed Tomography (CT) devices. We present a new linguistic scale for evaluating criteria and alternatives based on single-valued triangular neutrosophic numbers (SVTrN). The proposed approach is superior to other existing approaches due to its simplicity and ability to simulate natural human thinking via considering truth, indeterminacy, and falsity degrees. Then, applying it will increase the value of imaging for medical decision-making and decrease needless costs. So, this study can be valuable to researchers by helping them consider the appropriate medical imaging system selection problem theoretically under uncertainty, and for governments and organizations to design better satisfying medical imaging evaluation systems. [ABSTRACT FROM AUTHOR]

Cyber-attacks are getting increasingly complex, and as a result, the functional concerns of intrusion-detection systems (IDSs) are becoming increasingly difficult to resolve. The credibility of security services, such as privacy preservation, authenticity, and accessibility, may be jeopardized if breaches are not detected. Different organizations currently utilize a variety of tactics, strategies, and technology to protect the systems' credibility in order to combat these dangers. Safeguarding approaches include establishing rules and procedures, developing user awareness, deploying firewall and verification systems, regulating system access, and forming computer-issue management groups. The effectiveness of intrusion-detection systems is not sufficiently recognized. IDS is used in businesses to examine possibly harmful tendencies occurring in technological environments. Determining an effective IDS is a complex task for organizations that require consideration of many key criteria and their sub-aspects. To deal with these multiple and interrelated criteria and their sub-aspects, a multi-criteria decision-making (MCMD) approach was applied. These criteria and their sub-aspects can also include some ambiguity and uncertainty, and thus they were treated using q-rung orthopair fuzzy sets (q-ROFS) and q-rung orthopair fuzzy numbers (q-ROFNs). Additionally, the problem of combining expert and specialist opinions was dealt with using the q-rung orthopair fuzzy weighted geometric (q-ROFWG). Initially, the entropy method was applied to assess the priorities of the key criteria and their sub-aspects. Then, the combined compromised solution (CoCoSo) method was applied to evaluate six IDSs according to their effectiveness and reliability. Afterward, comparative and sensitivity analyses were performed to confirm the stability, reliability, and performance of the proposed approach. The findings indicate that most of the IDSs appear to be systems with high potential. According to the results, Suricata is the best IDS that relies on multi-threading performance. [ABSTRACT FROM AUTHOR]

Accurately estimating the unknown parameters of the photovoltaic (PV) models based on the measured voltage-current data is a challenging optimization problem due to its high nonlinearity and multimodality. An accurate solution to this problem is essential for efficiently simulating, controlling, and evaluating PV systems. There are three different PV models, including the single-diode model, the double-diode model, and the triple-diode model, with five, seven, and nine unknown parameters, respectively, proposed to represent the electrical characteristics of PV systems with varying levels of complexity and accuracy. In the literature, several deterministic and metaheuristic algorithms have been used to accurately solve this hard problem. However, due to the high nonlinearity of this problem, the deterministic methods could not achieve accurate solutions. On the other side, the metaheuristic algorithms, also known as gradient-free methods, could achieve somewhat good solutions for this problem, but they still need further improvements to strengthen their performance against stuck-in local optima and slow convergence speed problems. Over the last two years, several recent metaheuristic algorithms with better characteristics to improve convergence speed and avoid local optima have been proposed to tackle continuous optimization problems. However, the performance of the majority of those algorithms for estimating the parameters of PV models has not been investigated. Therefore, in this paper, the performance of nineteen recently published metaheuristic algorithms, such as the Mantis search algorithm (MSA), spider wasp optimizer (SWO), light spectrum optimizer (LSO), growth optimizer (GO), walrus optimization algorithm (WAOA), hippopotamus optimization algorithm (HOA), black-winged kite algorithm (BKA), quadratic interpolation optimization (QIO), sinh cosh optimizer (SCHA), exponential distribution optimizer (EDO), optical microscope algorithm (OMA), secretary bird optimization algorithm (SBOA), Parrot Optimizer (PO), Newton-Raphson-based optimizer (NRBO), crested porcupine optimizer (CPO), differentiated creative search (DCS), propagation search algorithm (PSA), one-to-one based optimizer (OOBO), and triangulation topology aggregation optimizer (TTAO), are studied to clarify their effectiveness in estimating the unknown parameters of PV models. In addition, those algorithms collaborate with two deterministic functions, namely the Lambert W-Function and the Newton-Raphson Method, to aid in solving the I-V curve equations more accurately, thereby improving the performance of PV systems. Those algorithms are assessed using four well-known PV solar cells and modules and compared with each other using several performance metrics, including best fitness, average fitness, worst fitness, standard deviation (SD), Friedman mean rank, and convergence speed; and a multiple-comparison test to compare the difference between their mean ranks. Results of this comparison show that SWO is more efficient and effective for SDM, DDM, and TDM over the majority of the studied PV solar cells and modules, and the Newton-Raphson Method is more efficient for solving the I-V curve equations. In addition, this study reports that the majority of the recently published metaheuristic algorithms perform poorly when applied to this problem. [ABSTRACT FROM AUTHOR]

Digital forensics is a proven method for collecting, preserving, reporting, analyzing, identifying, and presenting digital evidence from the original data, and it helps find evidence of cybercrimes. Intelligent multimedia forensics is a type of digital forensics and is essential because it is used to identify fake multimedia, including images, videos, audio, and text. This paper conducted a comprehensive survey for intelligent multimedia forensics, categorized into 3 classes: deep learning forensics, multimedia forensics, and network and Internet of Things forensics. In the first class, we provided a survey of the multiple attacks breaking the DL models, such as attacks in the training step, and the testing step, and crafted. In the craft attacks, we survey the three attacks: white box, gray box, and black box. We also provided some defense methods against DL attacks, training step attacks, and testing step attacks. In the second class, we offered a survey of multimedia forensics, including passive and active manipulation. In the third class, we provide a survey of network/IoT forensics, including the attacks in five layers: physical, data link, transport, application, and network. We also provided network/IoT attack detection in the third class using deep learning models. We applied AI models in various datasets and obtained higher accuracy and performance on the datasets from various used models. Finally, we offered the challenges and future direction for the researcher in this scope. • A complete survey of three classes of attacks in intelligent multimedia forensics is introduced. • The paper presents a comprehensive forensic survey of five layers in IoT attacks. • A comprehensive survey in deep learning forensics is presented. [ABSTRACT FROM AUTHOR]

• Introduces a Binary Young's Double-Slit Experiment optimizer (BYDSEX) for feature selection. • Utilizes tanh function to convert continuous solutions to binary for effective FS. • Integrates adaptive crossover with bitwise AND for enhanced exploration and diversity. • Demonstrates superior performance of BYDSEX on 31 large-scale datasets. • Effectively detects DDoS attacks using datasets from CIC-DDoS2019 and NSL-KDD. Contemporary advancements in technology provide vast quantities of data with large dimensions, leading to high computing burdens. These big data quantities suffer from irrelevant, redundant, and noisy features. Hence, Feature Selection (FS) has become a crucial task to identify the optimal subsets of features. This research proposes a Binary version of Young's Double-Slit Experiment optimizer (BYDSE) with crossover operation (BYDSEX) for tackling FS issues. Furthermore, the proposed algorithm employs the V-shaped transfer function to convert continuous solutions generated by the standard YDSE into binary ones. To assess the new solutions, we employ a well-known wrapper approach, K-Nearest Neighbors (KNN), which uses the Euclidean distance metric. We integrate an adaptive crossover with a bitwise AND operation into the suggested algorithm to enhance its exploration and population diversity. Moreover, the bitwise AND operation transfers the most informative and beneficial features to the new solutions. We compared BYDSEX with nine of the most recent and powerful algorithms using 31 large-scale datasets to demonstrate its efficacy. Moreover, our BYDSEX optimizer is utilized to detect the DDoS attacks faced by most IoT devices and contemporary technologies, using six datasets extracted from CIC-DDoS2019 and NSL-KDD. Various performance metrics are utilized to assess the algorithms, such as the accuracy, the selected feature size the fitness values, the fitness values, and the time. Two statistical tests are carried out, like paired-samples T and the Wilcoxon signed-rank. BYDSEX achieved superior results compared to its competitors for most of the datasets. Furthermore, BYDSEX obtains average accuracy values of 99.78%, 99.89%, 99.69% and 99.48% for LDAP and MSSQL, NETBIOS and NSL-KDD, respectively. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "A novel method for solving the fully neutrosophic linear programming problems" published in the journal Neural Computing & Applications. The article has been retracted due to concerns regarding compromised editorial handling and peer review process, inappropriate references, and being out of scope for the journal. The authors have not responded to correspondence regarding the retraction. Springer Nature, the publisher, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]

In recent years, unmanned aerial vehicles (UAVs) have been broadly employed as a data collection platform to assist in efficiently collecting data from IoT devices. However, the deployment optimization of UAVs has been challenged due to the need to minimize the energy consumption of UAVs and IoT devices. Several algorithms have been recently proposed for tackling this challenge, but they still have room for improvement due to their slow convergence speed and memory-wasting problems. Therefore, in this study, a new energy-aware approach has been proposed for accurately optimizing the entire deployment of UAVs, which could minimize the total energy consumption. This approach is based on presenting a new encoding mechanism, namely an optimized population size mechanism, for representing both location and number of stop points in an effective manner. In this mechanism, similar to some studies in the literature, the whole population is responsible for the entire deployment, and each individual is responsible for a stop point in this deployment. However, this mechanism presents a novel way to optimize the number of stop points based on adding an auxiliary variable to each stop point to determine whether it will be removed, inserted, or replaced in the newly generated deployment. This variable will be optimized by the optimization techniques during the optimization process to search for the optimal choice for each stop point that could achieve a better deployment. Two well-known optimization techniques, known as differential evolution (DE) and gradient-based optimizer (GBO), are adapted using this mechanism to present new variants, namely DEoPS and GBoPS, for accurately tackling the deployment optimization problem. Two energy consumption formulations are used in our work to investigate the performance of DEoPS and GBoPS. Several experiments have been conducted to compare the performance of both DEoPS and GBoPS with several algorithms on eleven instances. The experimental findings show the effectiveness of GBoPS for the first formulation and the effectiveness of DEoPS for the second formulation. [ABSTRACT FROM AUTHOR]

The article titled "Retraction Note: Grid quorum-based spatial coverage for IoT smart agriculture monitoring using enhanced multi-verse optimizer" has been retracted by the Editor-in-Chief and the publisher. The retraction was due to concerns regarding compromised editorial handling and peer review process, inappropriate or irrelevant references, and the article not being in scope of the journal or guest-edited issue. The authors have not responded to correspondence regarding this retraction. Springer Nature, the publisher, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]

Image segmentation is considered a crucial step required for image analysis and research. Many techniques have been proposed to resolve the existing problems and improve the quality of research, such as region-based, threshold-based, edge-based, and feature-based clustering in the literature. The researchers have moved toward using the threshold technique due to the ease of use for image segmentation. To find the optimal threshold value for a grayscale image, we improved and used a novel meta-heuristic equilibrium algorithm to resolve this scientific problem. Additionally, our improved algorithm has the ability to enhance the accuracy of the segmented image for research analysis with a significant threshold level. The performance of our algorithm is compared with seven other algorithms like whale optimization algorithm, bat algorithm, sine–cosine algorithm, salp swarm algorithm, Harris hawks algorithm, crow search algorithm, and particle swarm optimization. Based on a set of well-known test images taken from Berkeley Segmentation Dataset, the performance evaluation of our algorithm and well-known algorithms described above has been conducted and compared. According to the independent results and analysis of each algorithm, our algorithm can outperform all other algorithms in fitness values, peak signal-to-noise ratio metric, structured similarity index metric, maximum absolute error, and signal-to-noise ratio. However, our algorithm cannot outperform some algorithms in standard deviation values and central processing unit time with the large threshold levels observed. [ABSTRACT FROM AUTHOR]

To avoid any conflict toward a previous work [1], we clarify certain parts that need explanation and suggest some modifications that will enhance the performance of the suggested algorithm. We enhance the algorithm by making it work in any environment and under various conditions without the occurrence of any warnings. Moreover, we suggest various future directions that will help researchers in the application of neutrosophic analytic hierarchy process. Given that scientific research is always renewed and developed, we always strive to reach the best methods and solutions. Thus, we present this study as a good guide for researchers in their future works. [ABSTRACT FROM AUTHOR]

The demand for energy in Egypt has increased dramatically due to the steady increase in economic and societal development. To meet this need, the use of more renewable energy resources is an essential part of the solution to the ultimate shortage of energy. Because of the multitude of factors involved, the selection of the most suitable renewable energy systems (RESs) is a multi-criteria decision-making (MCDM) problem. There are a good number of works associated with the design of MCDM methods, particularly under uncertain and ambiguous situations. However, efficient incorporation of ambiguity and uncertainty in decision making is still a challenging task, and thus this work proposes a hybrid MCDM approach for selecting the components of a sustainable RES under uncertain environments, utilizing different triangular neutrosophic numbers to deal with unclear information. This proposed hybrid approach starts with defining the relative importance of the selected sustainable criteria by using an Analytic Hierarchy Process (AHP). Later, in the second phase, this hybrid approach combines VIKOR and the TOPSIS to rank the RESs for a real-life case study. The results obtained indicate that concentrated solar power is the most suitable source of renewable energy for Egypt, with photoelectric power the second most suitable. • Development of an evaluation model to select the most appropriate RES in Egypt. • A novel hybrid MCDM (AHP-VIKOR-TOPSIS) approach is proposed for RES selection. • Five criteria and their related sub-criteria are considered for such decision making. • The triangular neutrosophic numbers are introduced to dispose uncertainties. • A case study is proposed to illustrate the rationality of the proposed method. [ABSTRACT FROM AUTHOR]

Proton Exchange Membrane fuel cells (PEMFCs) are a promising renewable energy source to convert the chemical reactions between hydrogen and oxygen into electricity. To simulate, evaluate, manage, and optimize PEMFCs, an accurate mathematical model is essential. Therefore, this paper improves the accuracy of a mathematical model for the PEMFC based on semi-empirical equations by proposing a meta-heuristic technique to optimize its unidentified parameters. Because the I–V characteristic curve of the PEMFC systems has a nonlinear and multivariable nature, conventional optimization techniques are difficult and time-consuming but modern meta-heuristic algorithms are ideally suited. Therefore, in this paper, a new improved optimization algorithm based on the Heap-based optimizer (HBO) has been proposed to estimate the unknown parameters of PEMFCs models using an objective function that minimizes the error between the measured and estimated data. This improved HBO (IHBO) effectively uses two strategies: ranking-based position update (RPU) and Lévy-based exploitation improvement (LEI) to improve the final accuracy to the SSE value with higher convergence speed. Four well-known commercial PEMFCs, (the 500 W BCS stack, NetStack PS6, H-12 stack, and AVISTA SR-12 500 W modular) are utilized to verify the proposed IHBO and compare it with 11 popular optimizers using various performance metrics. The experimental findings show the superiority of IHBO in terms of convergence speed, stability, and final accuracy, where IHBO could fulfill fitness values of 0.01170, 2.14570, 0.11802, and 0.00014 for the 500 W BCS stack, NetStack PS6, H-12 stack, and AVISTA SR-12 500 W modular, respectively. • HBO is used for the first time to accurately identify PEMFC model parameters. • Four types of commercial PEMFC module are investigated in this work. • Intensive verifications with other challenging algorithms are implemented. • The accuracy, reliability, and convergence of the proposed algorithm are confirmed. • The superior performance of the proposed algorithm is verified in the experiments. [ABSTRACT FROM AUTHOR]

Hydrogen has a considerable number of uses in society so researchers continue to seek sustainable production methods in terms of the materials involved in the reaction at an appropriate and reasonable cost. In addition to its uses in a wide variety of chemical processes, hydrogen is also one of the new alternatives to energy because its combustion has a very low environmental impact and the heat content of combustion is almost three times the heat content of the same mass of oil fuel. Unfortunately, the majority of hydrogen production systems rely on fossil fuels, so considerable research is required to ensure that such production systems are more sustainable. Sustainability in hydrogen production entails not harming the environment and producing in a clean, safe, reliable, and affordable manner. Traditionally, hydrogen production options have not received much attention from researchers with regard to assessing environmental, economic, and social dimensions. Consequently, we seek in this study to complete a comprehensive evaluation of the sustainability of hydrogen production options through the use of a multi-criteria decision making model. Further, decisions taken by experts in the energy field are subject to uncertainty so this paper suggests a model to evaluate hydrogen production options by a group of experts based on a set of criteria under the neutrosophic environment. The relative importance of the selected criteria is evaluated by the Analytic Hierarchy Process and Complex Proportional Assessment and Evaluation based on Distance from Average Solution are adopted to rank the hydrogen production options. An illustrative case considering seven hydrogen production options was studied to verify the feasibility of the proposed framework, while results comparisons were implemented to indicate the advantages of the hybrid MCDM methods. All the results obtained indicate that the wind electrolysis method is the key to sustainable hydrogen production in the near future, followed by the biomass gasification approach. • A novel MCDM framework is proposed for evaluating sustainable HPOs. • Uncertainties were incorporated into the sustainability assessment framework. • Five main criteria and their seventeen sub-indicators have been used. • The AHP method was used to determine the weights of the criteria and sub-indicators. • A case study with seven hydrogen production options are considered to verify. [ABSTRACT FROM AUTHOR]

Customer lifetime value (CLV) is an essential measure to determine the level of profitability of a customer to a firm. Customer relationship management treats CLV as the most significant factor for measuring the level of purchases and, consequently, the profitability of a given customer. This motivates researchers to compete in developing models to maximize the value of CLV. Dynamic programming models in general--and the Q-learning model specifically--play a significant role in this area of research as a model-free algorithm. This maximizes the long-term future rewards of a certain agent, given their current state, set of possible actions, and the next state of that agent, assuming the customer represents the agent and CLV is their future reward. However, due to the stochastic nature of this problem, it is inaccurate to obtain a single crisp value for Q. In this paper, fuzzy logic and neutrosophic logic shall be utilized to search for the membership values of Q to capture the stochasticity and uncertainty of the problem. Both fuzzy Q-learning and neutrosophic Q-learning were implemented using two membership functions (i.e., trapezoidal, and triangular) to search for the optimal Q value that maximizes the customer's future rewards. The proposed algorithms were applied to two benchmark datasets: The Knowledge Discovery and Data Mining (KDD) cup 1998 direct mailing campaign dataset and the other from Kaggle, related to direct mailing campaigns. The proposed algorithms proved their effectiveness and superiority when comparing them to each other or the traditional deep Q-learning models. [ABSTRACT FROM AUTHOR]

• A Novel algorithm is proposed as the new method for identifying the parameters of solar PV. • The accuracy and convergence time of the proposed method is verified. • The parameters of single diode and double diode models of solar PV is estimated. • A set of feasible optimal cell parameters is achieved from several runs. • The significance of experimental data on cell I-V characteristic is validated. • Comparison with recent methods in the literature show the superiority of our proposed algorithm. In this paper, a recent optimization algorithm called Equilibrium Optimizer (EO) is first improved using a linear reduction diversity technique (LRD) and local minima elimination method (MEM). The improved EO (IEO) reduces the diversity of the population until enabling them to get better solutions. This method is centered around improving the particles with the worst fitness values within the population by moving them toward the best-so-far solution as an attempt to increase the convergence toward the near-optimal solution. As a side effect, LRD increases the probability of entrapment into local minima if it could not find a better solution. Therefore, another method known as local minima elimination method (MEM) is used to take the current solution either within the boundaries of two particles selected randomly or within the search boundaries of the problem itself. The extensive comparative experiments demonstrate that the proposed IEO is competitive and often superior compared to recent algorithms. We applied the proposed IEO algorithm to R.T.C France commercial solar cells using a single diode model (SDM), the double diode model (DDM), and three photovoltaic (PV) modules in addition to two commercial ones. [ABSTRACT FROM AUTHOR]

• IoT supportive technologies in IoT-based OSA diagnosis are summarized. • The researches of AI usage in IoT-based OSA diagnosis are discussed. • A new terminology "Internet of intelligent Things" is introduced. Obstructive Sleep Apnea (OSA) syndrome is one of the most widespread diseases that difficult to be detected and remedied. In particular, the examination of OSA by using the traditional Polysomnography (PSG) is one of formidable complexity as it requires full observation in a laboratory overnight. Meanwhile, the number of available laboratories and beds is minimal comparing to the number of OSA patients. What's more, the unusual environment and restricted mobility of patients may result in deficient diagnosis results. The Internet of Things (IoT) is the most appropriate solution for the previous diagnosis obstacles by allowing doctors to synchronize patient status. Besides, several studies have been introduced to consolidate the performance of IoT interoperability via the fusion with Artificial Intelligence (AI) resulting in the Internet of Intelligent Things (IoIT). This paper presents a literature survey about the intensification of IoT technologies for smart monitoring of sleep quality and OSA diagnosis. Mainly, the most recent enabling IoT and support technologies such as (smart devices, fog computing, cloud, big data, and machine learning) are covered via the discussion of more recent works of literature published from 2016 to 2019. Also, the roles of AI in optimizing the efficiency of OSA smart diagnosis are presented. Besides, a new comprehensive IoIT optimization framework is presented which employing AI for optimizing the performance of intelligent diagnosis of OSA. Finally, the open issues and challenges in this field are argued. This paper is, therefore, a major contributor to the compilation of all IoT innovative and efficient AI methods that improving the quality of OSA diagnosis. [ABSTRACT FROM AUTHOR]

Planning and managing resources is one of the most important topics in project management science. Resource leveling is used for improving work efficiency and minimizing cost throughout the life of the project. Fuzzy resource leveling models assume only truth-membership functions to deal uncertainties conditions surrounded by the projects and their activities duration. In this paper, we consider the objective function of scheduling problem is to minimize the costs of daily resource fluctuations using the precedence relationships during the project completion time. We design a resource leveling model based on neutrosophic set to overcome the ambiguity caused by the real-world problems. In this model, trapezoidal neutrosophic numbers are used to estimate the activities durations. The crisp model for activities time is obtained by applying score and accuracy functions. A numerical example is developed to illustrate the validation of the proposed model in this study. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) are the backbone in various modern Internet of Things (IoT) smart applications ranging from automated control, surveillance, forest fire detection, etc. One of the most important applications is the smart agriculture. The deployment of WSN in agricultural processes can predict crop yield, soil temperature, air quality, water level, crop price, and the appropriate time for market delivery which will help to increase productivity. In this paper, an enhanced metaheuristic algorithm called multi-verse optimizer with overlapping detection phase (DMVO) is introduced for optimizing the area coverage percentage of WSN. The proposed algorithm is tested on many datasets with different criterions and is compared with other algorithms including the original MVO, particle swarm optimization, and flower pollination algorithm. The experimental results are analyzed with one-way ANOVA test. In addition, DMVO is applied to IoT smart agriculture in East Oweinat area in Egypt and compared with Krill Herd algorithm. In addition, the experimental results are analyzed with Wilcoxon signed-rank test. The experimental results and the statistical analysis prove the prosperity and consistency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Some clarifications of a previous paper with the same title are presented here to avoid any reading conflict [1]. Also, corrections of some typo errors are underlined. Each modification is explained with details for making the reader able to understand the main concept of the paper. Also, some suggested modifications advanced by Singh et al. [3] (Journal of Intelligent & Fuzzy Systems, 2019, DOI:10.3233/JIFS-181541) are discussed. It is observed that Singh et al. [3] have constructed their modifications on several mathematically incorrect assumptions. Consequently, the reader must consider only the modifications which are presented in this research. [ABSTRACT FROM AUTHOR]

• Crested Porcupine Optimizer (CPO) is a nature-inspired meta-heuristic algorithm • CP is inspired by various defensive behavior to protect themselves against predators • CP uses four distinct protective mechanisms: sight, sound, odor, and physical attack • The first and second defensive techniques reflect the exploratory behavior of CPO • The third and fourth defensive strategies reflect the exploitative behavior of CPO In this paper, a novel nature-inspired meta-heuristic known as Crested Porcupine Optimizer (CPO) and inspired by various defensive behaviors of crested porcupine (CP) is proposed for accurately optimizing various optimization problems, especially those with large-scale. From least aggressive to most aggressive, the crowned porcupine uses four distinct protective mechanisms: sight, sound, odor, and physical attack. The first and second defensive techniques (sight and sound) reflect the exploratory behavior of CPO, whereas the third and fourth defensive strategies (odor and physical attack) reflect the exploitative behavior of CPO. The proposed algorithm presents a novel strategy called a cyclic population reduction technique to simulate the preposition that not all CPs activate their defense mechanisms, but only those threatened. This strategy promotes the convergence rate and population diversity. CPO was validated using three CEC benchmarks (CEC2014, CEC2017, and CEC2020), and its results were compared to those of three categories of existing optimization algorithms, as follows: (i) the most highly-cited optimizers, including gray wolf optimizer (GWO), whale optimization algorithm (WOA), differential evolution, and salp swarm algorithm (SSA); (ii) recently published algorithms, including gradient-based optimizer (GBO), African vultures optimization algorithm (AVOA), Runge Kutta method (RUN), Equilibrium Optimizer (EO), Artificial Gorilla Troops Optimizer (GTO), and Slime Mold Algorithm (SMA); and (iii) high-performance optimizers, such as SHADE, LSHADE, AL-SHADE, LSHADE-cnEpSin, and LSHADE-SPACMA. The statistical analysis revealed that CPO can be nominated as a high-performance optimizer because it had a significantly superior performance in comparison to all competing optimizers for the majority of the test functions in three validated CEC benchmarks. Quantitively, CPO could achieve an improvement rate over the rival optimizers with a percentage up to 83% for CEC2017, 70% for CEC2017, 90% for CEC2020, and 100% for six real-world engineering problems. The source code of CPO is publicly accessible at https://drive.matlab.com/sharing/24c48ec7-bfd5-4c22-9805-42b7c394c691/ [ABSTRACT FROM AUTHOR]

The project selection is one of the most important phases of a project life cycle. The project selection is considered as a Multi-Criteria Decision-Making (MCDM) problem. This research aims to study the integration between Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) into Decision-Making Trial and Evaluation Laboratory (DEMATEL) under neutrosophic environment to provide a new technique for making a decision regarding the choice of appropriate project (project selection) as one of the most important phases of the project life cycle. Projects are selected by comparing them against many criteria. Criteria are evaluated based on expert's opinion. Sometimes experts cannot give reliable information due to the non-deterministic environment. The neutrosophic set theory will be used to handle and overcome the ambiguity or lack of confirmation of information. The criteria are weighted by DEMATEL, then the best project alternative is selected using TOPSIS. In the proposed model, each pairwise comparison judgments is symbolized as a trapezoidal neutrosophic number. Experts will focus only on (n − 1) judgments for n alternatives to overcome the difficulties of [(n * (n − 1))/2] consistence judgments in case of increasing number of alternatives. A numerical example is developed to show the validation of the suggested model in the neutrosophic environment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new binary version of the flower pollination algorithm (BFPA) for solving 0–1 knapsack problem. The standard flower pollination algorithm (FPA) is used for the continuous optimization problems. So, a transformation function is used to convert the continuous values generated from FPA into binary ones. A penalty function is added to the evaluation function to give negative values for the infeasible solutions. The infeasible solutions are treated by using a two-stage repair operator called flower repair. Experimental results have proved the superiority of BFPA over other algorithms. [ABSTRACT FROM AUTHOR]

The consolidation of virtual machine (VM) is the strategy of efficient and intelligent use of cloud datacenters resources. One of the important subproblems of VM consolidation is VM placement problem. The main objective of VM placement problem is to minimize the number of running physical machines or hosts in cloud datacenters. This paper focuses on solving VM placement problem with respect to the available bandwidth which is formulated as variable sized bin packing problem. Moreover, a new bandwidth allocation policy is developed and hybridized with an improved variant of whale optimization algorithm (WOA) called improved Lévy based whale optimization algorithm. Cloudsim toolkit is used in order to test the validity of the proposed algorithm on 25 different data sets that generated randomly and compared with many optimization algorithms including: WOA, first fit, best fit, particle swarm optimization, genetic algorithm, and intelligent tuned harmony search. The obtained results are analyzed by Friedman test which indicates the prosperity of the proposed algorithm for minimizing the number of running physical machine. [ABSTRACT FROM AUTHOR]

The most widely used technique for solving and optimizing a real-life problem is linear programming (LP), due to its simplicity and efficiency. However, in order to handle the impreciseness in the data, the neutrosophic set theory plays a vital role which makes a simulation of the decision-making process of humans by considering all aspects of decision (i.e., agree, not sure and disagree). By keeping the advantages of it, in the present work, we have introduced the neutrosophic LP models where their parameters are represented with a trapezoidal neutrosophic numbers and presented a technique for solving them. The presented approach has been illustrated with some numerical examples and shows their superiority with the state of the art by comparison. Finally, we conclude that proposed approach is simpler, efficient and capable of solving the LP models as compared to other methods. [ABSTRACT FROM AUTHOR]

Cyber–physical–social systems refer to new systems that involve a large number of Internet of Things (IoT) devices connected through an interconnected network. These systems gather data for various services such as transportation, energy, healthcare, etc. This data is then transmitted to the cloud layer for storage, processing, and access. To handle the significant workload offloaded from resource-constrained IoT devices without latency or bandwidth issues, a technology called fog computing has emerged as an intermediary layer between the cloud and IoT devices. The quality of service (QoS) provided by fog computing relies on efficient task scheduling to optimize energy consumption and maximum execution time. This task scheduling problem is known to be NP-hard and cannot be solved with high precision in a reasonable time using traditional techniques or existing metaheuristic-based task schedulers. Therefore, this study proposes a new multi-objective task scheduling approach based on a modified marine predators algorithm (MMPA) to simultaneously minimize energy consumption and make-span under the Pareto optimality theory. This variant is named the multi-objective MMPA (M2MPA). M2MPA enhances the performance of the MPA by incorporating the polynomial crossover operator to improve its exploration operator and the adaptive CF parameter to enhance its exploitation operator. The effectiveness of M2MPA is evaluated using eighteen tasks with different scales (small, large, and medium) and heterogeneous workloads assigned to two hundred fog devices with varying processing speeds. Finally, M2MPA is compared with six well-established techniques using various performance indicators, including carbon dioxide emission rate, flowtime, make-span, and energy consumption. The experimental findings demonstrate the substantial superiority of M2MPA over all competing algorithms across different performance indicators and the Wilcoxon rank-sum test. Quantitatively, M2MPA achieves an average make-span of 10.095, average energy consumption of 3.77E+06, average flowtime of 8.39E+03, and average carbon dioxide emission rate of 8.88E+06. [ABSTRACT FROM AUTHOR]

This study presents a new nature-inspired optimization algorithm, namely the Mantis Search Algorithm (MSA), inspired by the unique hunting behavior and sexual cannibalism of praying mantises. In brief, MSA consists of three optimization stages, including the search for prey (exploration), attack prey (exploitation), and sexual cannibalism. Those operators are simulated using various mathematical models to effectively tackle optimization challenges across diverse search spaces. The performance of MSA is rigorously tested on fifty-two optimization problems and three real-world applications (five engineering design problems, and the parameter estimation problem of photovoltaic modules and fuel cells) to show its versatility and adaptability to different scenarios. To disclose the MSA's superiority, it is compared to two categories from the rival optimizers: the first category involves well-established and highly-cited optimizers, like Differential evolution; and the second category contains recently-published algorithms, like African Vultures Optimization Algorithm. This comparison is conducted using several performance metrics, the Wilcoxon rank-sum test and the Friedman mean rank to disclose the MSA's effectiveness and efficiency. The results of this comparison highlight the effectiveness of this new approach and its potential for future optimization applications. The source codes of the MSA algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/131833-mantis-search-algorithm-msa. • A nature-inspired algorithm named mantis search algorithm (MSA) was proposed. • MSA simulates the hunting behavior and sexual cannibalism of praying mantises. • MSA was assessed using 52 test problems and 6 engineering design problems. • MSA is compared with nine published over different benchmarks problems. [ABSTRACT FROM AUTHOR]

In group decision-making (GDM) model, experts often evaluate their opinion by using triangular fuzzy numbers. The preference relations with triangular fuzzy numbers are in consistent in nature, so we turned to neutrosophic in this paper. It is very important to take into account consistency of expert opinion and consensus degree in GDM. In order to distinguish the typical consistency, the concept of additive approximation consistency is proposed for triangular neutrosophic additive reciprocal matrices. The properties of triangular neutrosophic additive reciprocal matrices with additive approximation consistency are studied in detail. Second, by using (n-1) restricted preference values, a triangular neutrosophic additive reciprocal preference relation with additive approximation consistency is constructed. The differences among expert’s opinions are measured using consensus degree. For generating a collective triangular neutrosophic additive reciprocal matrix with additive approximation consistency, the neutrosophic triangular weighted aggregation operator is used. Finally, a novel algorithm for the group decision-making problem with triangular neutrosophic additive reciprocal preference relations is presented. A numerical example is carried out to illustrate the proposed definitions and algorithm. [ABSTRACT FROM AUTHOR]

Bin packing problem (BPP) is a classical combinatorial optimization problem widely used in a wide range of fields. The main aim of this paper is to propose a new variant of whale optimization algorithm named improved Lévy-based whale optimization algorithm (ILWOA). The proposed ILWOA adapts it to search the combinatorial search space of BPP problems. The performance of ILWOA is evaluated through two experiments on benchmarks with varying difficulty and BPP case studies. The experimental results confirm the prosperity of the proposed algorithm in proficiency to find the optimal solution and convergence speed. Further, the obtained results are discussed and analyzed according to the problem size. [ABSTRACT FROM AUTHOR]

With the advance of the communication technology and the massive flow of information across the internet, it is becoming urgent to keep the confidentiality of the transmitted information. Using the internet has been extended to several fields such as e-mail, e-commerce, e-learning, health and medicine, shopping, and so on. Cryptography is the study of different techniques for securing the communication between the sender and the receiver. One of the most known cryptosystems is Merkle-Hellman Knapsack Cryptosystem (MHKC). It is one of the earliest Public Key Cryptosystem (PKC) that is used to secure the messages between the sender and the receiver. Developing a powerful cryptosystem comes after studying the fragility points of the current cryptosystems. The Whale Optimization Algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social behavior of humpback whales. WOA has validated excellent performance in solving the continuous problems and the engineering optimization problems. This paper introduces a novel Modified version of WOA (MWOA) for cryptanalysis of MHKC. The sigmoid function is used to map the continuous values into discrete one. A penalty function is added to the evaluation function to deal with the infeasible solutions. The mutation operation is employed for improving the solutions. The results show that MWOA is more effective and robust than other algorithms in the literature. [ABSTRACT FROM AUTHOR]

During the recent years the smart cities knows a great extension as a modern shape of sustainable expansion. It's a urban area that utilize various devices connected with internet and integrates them with ICTs to promote goodness and execution of services for the best interaction among citizens and city's government. The basic for smart cities is distributed and independent information infrastructure. Using information effectively is going to be a main factor for success in the smart cities. The sources of information's (models, experts, and sensors) must be reason, perfect and complete. The generated information from independent and distributed sources can be imprecise, uncertain, and/or incomplete in real life. Any deficiency in gathered information will have a negative effect on the performance of services and decision making process within smart cities. So, we need a general framework to represent all types of imperfect and incomplete information. Since the classical methods fails to deal with vague, inconsistent and incomplete information, the fuzzy set was introduced to solve this drawback. The fuzzy set was not the perfect method for dealing with these drawbacks because it considers only truthiness and fails to deal with indeterminacy. The efficient mathematical tool for dealing with uncertain, vague and inconsistent objects is rough sets theory which introduced by Pawlak. The theory of neutrosophic rough sets is powerful for dealing with incompleteness and neutrosophic set deals with indeterminate and inconsistent data efficiently through considering truthiness, indeterminacy and falsity degrees. So, in this research we will propose a general framework for dealing with imperfect and incomplete information through using single valued neutrosophic and rough set theories. The combination of two sets will deal with all aspects of vagueness, inconsistency and incompleteness of data and information, and then will enhance the quality of introduced services and decisions from smart cities to their citizens. As experimentation, we applied the proposed framework for modeling imperfect and incomplete data in healthcare field. [ABSTRACT FROM AUTHOR]