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In late 2023, the United Nations conference on climate change (COP28), which was held in Dubai, encouraged a quick move from fossil fuels to renewable energy. Solar energy is one of the most promising forms of energy that is both sustainable and renewable. Generally, photovoltaic systems transform solar irradiance into electricity. Unfortunately, instability and intermittency in solar radiation can lead to interruptions in electricity production. The accurate forecasting of solar irradiance guarantees sustainable power production even when solar irradiance is not present. Batteries can store solar energy to be used during periods of solar absence. Additionally, deterministic models take into account the specification of technical PV systems and may be not accurate for low solar irradiance. This paper presents a comparative study for the most common Deep Learning (DL) and Machine Learning (ML) algorithms employed for short-term solar irradiance forecasting. The dataset was gathered in Islamabad during a five-year period, from 2015 to 2019, at hourly intervals with accurate meteorological sensors. Furthermore, the Grid Search Cross Validation (GSCV) with five folds is introduced to ML and DL models for optimizing the hyperparameters of these models. Several performance metrics are used to assess the algorithms, such as the Adjusted R2 score, Normalized Root Mean Square Error (NRMSE), Mean Absolute Deviation (MAD), Mean Absolute Error (MAE) and Mean Square Error (MSE). The statistical analysis shows that CNN-LSTM outperforms its counterparts of nine well-known DL models with Adjusted R2 score value of 0.984. For ML algorithms, gradient boosting regression is an effective forecasting method with Adjusted R2 score value of 0.962, beating its rivals of six ML models. Furthermore, SHAP and LIME are examples of explainable Artificial Intelligence (XAI) utilized for understanding the reasons behind the obtained results. [ABSTRACT FROM AUTHOR]

The optimization challenge known as the optimal reactive power dispatch (ORPD) problem is of utmost importance in the electric power system owing to its substantial impact on stability, cost-effectiveness, and security. Several metaheuristic algorithms have been developed to address this challenge, but they all suffer from either being stuck in local minima, having an insufficiently fast convergence rate, or having a prohibitively high computational cost. Therefore, in this study, the performance of four recently published metaheuristic algorithms, namely the mantis search algorithm (MSA), spider wasp optimizer (SWO), nutcracker optimization algorithm (NOA), and artificial gorilla optimizer (GTO), is assessed to solve this problem with the purpose of minimizing power losses and voltage deviation. These algorithms were chosen due to the robustness of their local optimality avoidance and convergence speed acceleration mechanisms. In addition, a modified variant of NOA, known as MNOA, is herein proposed to further improve its performance. This modified variant does not combine the information of the newly generated solution with the current solution to avoid falling into local minima and accelerate the convergence speed. However, MNOA still needs further improvement to strengthen its performance for large-scale problems, so it is integrated with a newly proposed improvement mechanism to promote its exploration and exploitation operators; this hybrid variant was called HNOA. These proposed algorithms are used to estimate potential solutions to the ORPD problem in small-scale, medium-scale, and large-scale systems and are being tested and validated on the IEEE 14-bus, IEEE 39-bus, IEEE 57-bus, IEEE 118-bus, and IEEE 300-bus electrical power systems. In comparison to eight rival optimizers, HNOA is superior for large-scale systems (IEEE 118-bus and 300-bus systems) at optimizing power losses and voltage deviation; MNOA performs better for medium-scale systems (IEEE 57-bus); and MSA excels for small-scale systems (IEEE 14-bus and 39-bus systems). [ABSTRACT FROM AUTHOR]

Early in 2019, COVID-19 was discovered for the first time in Wuhan, China, resulting in the deaths of a significant number of people in many different countries all over the world. Due to the rapid spread of this epidemic, scientists have strived to find quick and accurate diagnostic methods to lessen its global impact. Chest X-ray images were the best tool for rapidly and safely detecting COVID-19, but the manual examination of those images might result in faulty diagnoses. Therefore, the scientists have used deep learning (DL) models to remedy this shortcoming and classify the images infected with COVID-19 more accurately. Image segmentation is an essential step in improving the classification accuracy of DL models. Among existing image segmentation techniques, multilevel thresholding-based image segmentation techniques have gained significant interest due to their simplicity and high accuracy. However, the computational cost of those techniques exponentially increases as the number of threshold levels increases. Therefore, over the last few years, metaheuristic algorithms have collaborated with those techniques to significantly lessen the computational cost and accurately solve the image segmentation problem. However, those algorithms have some shortcomings, such as falling into local minima and slow convergence speed, which make them unable to find precise results. Therefore, in this paper, we present a new multilevel thresholding-based medical image segmentation technique based on the recently proposed spider wasp optimizer (SWO) to better segment the medical images, especially the chest X-ray images for detecting COVID-19 infection more accurately and rapidly. In addition, SWO is enhanced by two newly proposed mechanisms, namely global search improvement and local search improvement, to present a new better variant, namely improved SWO (ISWO). The former mechanism is responsible for improving the exploration operator by sharing the knowledge of the current individual and a newly generated individual, while the latter aims to improve the exploitation operator to improve the convergence speed. To evaluate the stability of ISWO and SWO, ten COVID-19 X-ray images with heterogeneous histograms under nine threshold levels (T) are used. Also, they are compared to eight rival optimizers according to several performance metrics to demonstrate their efficacy. According to the experimental results, ISWO is the best-performing algorithm, followed by SWO. Quantitatively, ISWO could achieve an average fitness value of 2796.837, while SWO could reach a value of 2796.33. [ABSTRACT FROM AUTHOR]

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]

Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks. [ABSTRACT FROM AUTHOR]

Waste management is a difficult and complicated issue. Since this waste may constitute a threat to persons and the environment, it is vital to guarantee that it is adequately collected. Therefore, new waste collection technologies that adopt modern technology and the Internet of Things (IoT) are the appropriate alternative. Determining the optimal intelligent technology for waste management and tuning its priorities is a complicated task that requires taking into account the dimensions of environmental, economic, and social sustainability. Thus, this paper introduces a hybrid methodology for multi-criteria decision-making (MCDM) that assesses intelligent waste management technologies utilizing IoT, taking into account multiple criteria. First, eleven decision-making criteria are determined to give a realistic approach. Second, the researchers demonstrated the innovative decision approach established on the combination of the Measurement of Alternatives and Ranking according to the COmpromise Solution (MARCOS) method and the Indifference Threshold-based Attribute Ratio Analysis (ITARA) method, called T2NN-ITARA, under a type-2 neutrosophic numbers (T2NNs) environment. This approach has been used to define the criteria's relative significance. Also, T2NN-MARCOS approach has been established to evaluate and classify intelligent waste management technologies based on IoT and to reveal the most sustainable solution. An illustrative case study evaluating four intelligent waste management technologies based on IoT is presented to prove the validity of the applied methodology. The findings show that the criteria of sustainability and standardization are the two most influential criteria in the evaluation and classification of intelligent waste management technologies based on IoT. It has also been determined that the RFID and GPRS blend for waste management is the most suitable intelligent technology for garbage management. Sensitivity and comparison analyzes were also accomplished to illustrate the stability, strength, and robustness of the suggested approach. The research provides significant information for government and waste practitioners. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have achieved a great deal of success, indicating that the motor industry will soon be emission-free. They run on electricity stored in batteries, which their drivers can recharge using an external source of electricity. Therefore, the development of an infrastructure for charging EVs has become a necessity. In this paper, a real case study in Zagazig, Egypt, for evaluating, operating, and determining the most sustainable location for an EV charging station is presented. Identifying such a location is a complex task that requires considering many influencing factors and their sub-factors. Consequently, a general evaluation system that includes six key factors, in addition to nineteen sub-factors, has been developed. A multi-criteria decision-making (MCDM) approach is applied to deal with this problem. The neutrosophic theory with type-2 neutrosophic numbers (T2NNs) and a type-2 neutrosophic number weighted averaging (T2NNWA) operator are used to combine expert and specialized views since they are ambiguous and uncertain. Firstly, the Decision-making Trial and Evaluation Laboratory (DEMATEL) method is applied to prioritize key factors and sub-factors. Then, the Complex Proportional Assessment (COPRAS) technique selects six sustainable sites for EV charging stations. Also, comparison and sensitivity evaluations verify the approach's reliability, strength, stability, and performance. Finally, this article provides good information for policymakers and the results of the case study indicate that the economic factor is the most important factor in selecting the optimal location followed by the technical factor. [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]

The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs using the k-means clustering algorithm. Then, a low-complexity greedy mechanism is used to generate the order of HPs assigned to each UAV, determining its trajectory. Several experiments are conducted to assess the effectiveness of the MTPAs with variable population size and cyclic selection mechanisms. The experimental findings demonstrate that the MTPAs with the cyclic selection mechanism outperform all competing algorithms, achieving better outcomes. [ABSTRACT FROM AUTHOR]

Chest diseases, especially COVID-19, have quickly spread throughout the world and caused many deaths. Finding a rapid and accurate diagnostic tool was indispensable to combating these diseases. Therefore, scientists have thought of combining chest X-ray (CXR) images with deep learning techniques to rapidly detect people infected with COVID-19 or any other chest disease. Image segmentation as a preprocessing step has an essential role in improving the performance of these deep learning techniques, as it could separate the most relevant features to better train these techniques. Therefore, several approaches were proposed to tackle the image segmentation problem accurately. Among these methods, the multilevel thresholding-based image segmentation methods won significant interest due to their simplicity, accuracy, and relatively low storage requirements. However, with increasing threshold levels, the traditional methods have failed to achieve accurate segmented features in a reasonable amount of time. Therefore, researchers have recently used metaheuristic algorithms to tackle this problem, but the existing algorithms still suffer from slow convergence speed and stagnation into local minima as the number of threshold levels increases. Therefore, this study presents an alternative image segmentation technique based on an enhanced version of the Kepler optimization algorithm (KOA), namely IKOA, to better segment the CXR images at small, medium, and high threshold levels. Ten CXR images are used to assess the performance of IKOA at ten threshold levels (T-5, T-7, T-8, T-10, T-12, T-15, T-18, T-20, T-25, and T-30). To observe its effectiveness, it is compared to several metaheuristic algorithms in terms of several performance indicators. The experimental outcomes disclose the superiority of IKOA over all the compared algorithms. Furthermore, the IKOA-based segmented CXR images at eight different threshold levels are used to train a newly proposed CNN model called CNN-IKOA to find out the effectiveness of the segmentation step. Five performance indicators, namely overall accuracy, precision, recall, F1-score, and specificity, are used to disclose the CNN-IKOA's effectiveness. CNN-IKOA, according to the experimental outcomes, could achieve outstanding outcomes for the images segmented at T-12, where it could reach 94.88% for overall accuracy, 96.57% for specificity, 95.40% for precision, and 95.40% for recall. [ABSTRACT FROM AUTHOR]

This work presents a new nature-inspired meta-heuristic algorithm named spider wasp optimization (SWO) algorithm, which is based on replicating the hunting, nesting, and mating behaviors of the female spider wasps in nature. This proposed algorithm has various unique updating strategies, making it applicable to a wide range of optimization problems with different exploration and exploitation requirements. The proposed SWO is compared with nine newly published and well-established metaheuristics over four different benchmarks: (1) Standard benchmark, including 23 unimodal and multimodal test functions; (2) test suite of CEC2017, (3) test suite of CEC2020, and (4) test suite of CEC2014 to validate its reliability. Moreover, two classical engineering design problems, namely, welded bean and pressure vessel designs, and parameter estimation of the single-diode, double-diode, and triple-diode photovoltaic models are used to further evaluate the performance of SWO in optimizing real-world optimization problems. Experimental findings demonstrate that SWO is more competitive compared with the state-of-art meta-heuristic methods for four validated benchmarks and superior to all observed real-world optimization problems. Specifically, SWO achieves an overall effective percentage of 78.2% on the standard benchmark, 92.31% on CEC2014, 77.78% on CEC2017, 60% on CEC2020, and 100% on real-world problems. The source code of SWO is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/126010-spider-wasp-optimizer-swo. [ABSTRACT FROM AUTHOR]

Numerous optimization problems can be addressed using metaheuristics instead of deterministic and heuristic approaches. This study proposes a novel population-based metaheuristic algorithm called the Exponential Distribution Optimizer (EDO). The main inspiration for EDO comes from mathematics based on the exponential probability distribution model. At the outset, we initialize a population of random solutions representing multiple exponential distribution models. The positions in each solution represent the exponential random variables. The proposed algorithm includes two methodologies for exploitation and exploration strategies. For the exploitation stage, the algorithm utilizes three main concepts, memoryless property, guiding solution and the exponential variance among the exponential random variables to update the current solutions. To simulate the memoryless property, we assume that the original population contains only the winners that obtain good fitness. We construct another matrix known as memoryless to retain the newly generated solutions regardless of their fitness compared to their corresponding winners in the original population. As a result, the memoryless matrix stores two types of solutions: winners and losers. According to the memoryless property, we disregard and do not memorize the previous history of these solutions because past failures are independent and have no influence on the future. The losers can thus contribute to updating the new solutions next time. We select two solutions from the original population derived from the exponential distributions to update the new solution throughout the exploration phase. Furthermore, EDO is tested against classical test functions in addition to the Congress on Evolutionary Computation (CEC) 2014, CEC 2017, CEC 2020 and CEC 2022 benchmarks, as well as six engineering design problems. EDO is compared with the winners of CEC 2014, CEC 2017 and CEC 2020, which are L-SHADE, LSHADE−cnEpSin and AGSK, respectively. EDO reveals exciting results and can be a robust tool for CEC competitions. Statistical analysis demonstrates the superiority of the proposed EDO at a 95% confidence interval. [ABSTRACT FROM AUTHOR]

The selection and assessment process of appropriate robots became a more complex and complicated task due to various available alternatives and conflicting attributes which must take into consideration. Also, uncertainty which exists usually in the selection process is an unavoidable component that needs to be thoughtfully measured and traditional multi-attribute decision-making approaches failed to deal precisely with it. Since almost all decisions originate from subjective ordinal preferences, handling uncertainty using linguistic variables is also not enough. Thus, the objective of the current study is to present a new extended ordinal priority approach in the neutrosophic environment for the first time to select an appropriate robot. Since neutrosophic is one of the most effective and accommodating tools for handling uncertainty, thus, this method goes to transform linguistic information into triangular neutrosophic numbers using a new presented scale. This scale was used to determine the importance degree of attributes and alternatives regarding experts' opinions. Also, the score function of the triangular neutrosophic number is used for prioritizing attributes and alternatives. The experts in our proposed method have the same degree of importance, since each expert is a person with special skills and knowledge representing mastery of a particular subject. To measure the applicability and efficiency of the proposed approach, an experimental case study has been established for the robot selection problem of a new pharmaceutical city in Egypt for the first time. The source of data in this case study is experts, interviews, and questionnaires. Also, sensitivity and comparative analysis are further made for verifying the power of the proposed approach. The outcome of this study shows that the suggested approach for robot selection is quite helpful and has a great performance under uncertainty over classical and fuzzy ordinal priority approaches. Also, the suggested approach is less consumption of time and simpler than the fuzzy ordinal priority approach. Therefore, we recommend firms and governments to apply it for increasing product quality, hence the profitability of manufacturing industries and decrease needless costs. [ABSTRACT FROM AUTHOR]

The separation of an object from other objects or the background by selecting the optimal threshold values remains a challenge in the field of image segmentation. Threshold segmentation is one of the most popular image segmentation techniques. The traditional methods for finding the optimum threshold are computationally expensive, tedious, and may be inaccurate. Hence, this paper proposes an Improved Whale Optimization Algorithm (IWOA) based on Kapur's entropy for solving multi-threshold segmentation of the gray level image. Also, IWOA supports its performance using linearly convergence increasing and local minima avoidance technique (LCMA), and ranking-based updating method (RUM). LCMA technique accelerates the convergence speed of the solutions toward the optimal solution and tries to avoid the local minima problem that may fall within the optimization process. To do that, it updates randomly the positions of the worst solutions to be near to the best solution and at the same time randomly within the search space according to a certain probability to avoid stuck into local minima. Because of the randomization process used in LCMA for updating the solutions toward the best solutions, a huge number of the solutions around the best are skipped. Therefore, the RUM is used to replace the unbeneficial solution with a novel updating scheme to cover this problem. We compare IWOA with another seven algorithms using a set of well-known test images. We use several performance measures, such as fitness values, Peak Signal to Noise Ratio, Structured Similarity Index Metric, Standard Deviation, and CPU time. [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]

These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E-Train, E-Ambulance has been growing progressively. These applications require mobility-aware delay-sensitive services to execute their tasks. With this motivation, the study has the following contribution. Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container microservices which offers cost-efficient and mobility-aware services with rich resources for processing. This study devises the cost-efficient task offloading and scheduling (CEMOTS) algorithm framework, which consists of the mobility aware task offloading phase (MTOP) method, which determines the optimal offloading time to minimize the communication cost of applications. Furthermore, CEMOTS offers Cooperative Task Offloading Scheduling (CTOS), including task sequencing and scheduling. The goal is to reduce the application costs of communication cost and computational costs under a given deadline constraint. Performance evaluation shows the CTOS and MTOP outperform existing task offloading and scheduling methods in the VCFN in terms of costs and the deadline for IoT applications. [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]

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]

Naturally, to analyze an image accurately, all the similar objects within it should be separated to pay attention to the most important object for reaching more details and hence achieving better accuracy. Therefore, multilevel thresholding is an indispensable image processing technique in the field of image segmentation and is employed widely to separate those similar objects. However, with increasing thresholds, the existing image segmentation techniques might suffer from exponentially-grown computational cost and low accuracy due to local optima shortage. Therefore, in this paper, a new image segmentation algorithm based on the improved marine predators algorithm (MPA) is proposed. MPA is improved using a strategy to find a number of the worst solutions within the population then tries to search for other better ones for those solutions by moving them gradually towards the best solutions to avoid accelerating to local optima and randomly within the search space based on a certain probability. In addition, this number of the worst solutions is increased with the iteration. This strategy is known as the linearly increased worst solutions improvement strategy (LIS). Also, we suggested that apply the ranking strategy based on a novel updating scheme, namely ranking-based updating strategy (RUS), on the solutions that could find better solutions in the last number iterations, perIter, in the hope of finding better solutions near it. RUS updates the particles/solutions which could not find better solutions than the best-local one in a number of consecutive iterations, with those that are generated based on a novel updating strategy. LIS is integrated with MPA to produce a new segmentation meta-heuristic algorithm abbreviated as MPALS. Also, MPALS and RUS are combined to tackle ISP in a strong variant abbreviated as HMPA for overcoming the image segmentation problem. The two proposed algorithms are validated on 14 test images and compared with seven state-of-the-arts meta-heuristic algorithms. The experimental results show the effectiveness of HMPA with increasing the threshold levels compared to the seven state-of-the-arts algorithms when segmenting an image, while their performance is roughly the same for the image with a small threshold level. [ABSTRACT FROM AUTHOR]

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]

The significant growth of modern technology and smart systems has left a massive production of big data. Not only are the dimensional problems that face the big data, but there are also other emerging problems such as redundancy, irrelevance, or noise of the features. Therefore, feature selection (FS) has become an urgent need to search for the optimal subset of features. This paper presents a hybrid version of the Harris Hawks Optimization algorithm based on Bitwise operations and Simulated Annealing (HHOBSA) to solve the FS problem for classification purposes using wrapper methods. Two bitwise operations (AND bitwise operation and OR bitwise operation) can randomly transfer the most informative features from the best solution to the others in the populations to raise their qualities. The Simulate Annealing (SA) boosts the performance of the HHOBSA algorithm and helps to flee from the local optima. A standard wrapper method K-nearest neighbors with Euclidean distance metric works as an evaluator for the new solutions. A comparison between HHOBSA and other state-of-the-art algorithms is presented based on 24 standard datasets and 19 artificial datasets and their dimension sizes can reach up to thousands. The artificial datasets help to study the effects of different dimensions of data, noise ratios, and the size of samples on the FS process. We employ several performance measures, including classification accuracy, fitness values, size of selected features, and computational time. We conduct two statistical significance tests of HHOBSA like paired-samples T and Wilcoxon signed ranks. The proposed algorithm presented superior results compared to other algorithms. [ABSTRACT FROM AUTHOR]

Due to increased demand for video data by heterogeneous Internet users, the demand of multicasting is increasing day by day. Multicasting is an efficient group communication technique, which is widely used in various applications such as bloggers, Internet group, forums, conferences, YouTube and online TV. Because of the heterogeneous nature of receivers, the network become congested that results in high packet loss, less throughput and reduced QoS. The multicast congestion control seems to be an effective solution to tackle the congestion issue in which the reception rate is adjusted according to the feedback of receivers. This paper provides a new congestion control scheme for multicast communication called Extended Logarithmic Increase and Multiplicative Decrease (ELIMD) to reduce packet loss, increase throughput, QoS and fairness during group communication. The entire research work is classified and elaborated in the key components namely architecture, newly designed equations, and flow charts. Experimental validation in NS-2.35 has affirmed the efficiency of the proposed scheme ELIMD against the existing schemes. [ABSTRACT FROM AUTHOR]

In a developed society, people have more concerned about their health. Thus, improvement of medical field application has been one of the greatest active study areas. Medical statistics show that heart disease is the main reason for morbidity and death in the world. The physician's job is difficult because of having too many factors to analyze in the diagnosis of heart disease. Besides, data and information gained by the physician for diagnosis are often partial and immersed. Recently, health care applications with the Internet of Things (IoT) have offered different dimensions and other online services. These applications have provided a new platform for millions of people to receive benefits from the regular health tips to live a healthy life. In this paper, we propose a novel framework based on computer supported diagnosis and IoT to detect and monitor heart failure infected patients, where the data are attained from various other sources. The proposed healthcare system aims at obtaining better precision of diagnosis with ambiguous information. We suggest neutrosophic multi criteria decision making (NMCDM) technique to aid patient and physician to know if patient is suffering from heart failure. Furthermore, through dealing with the uncertainty of imprecision and vagueness resulted from the symmetrical priority scales of different symptoms of disease, users know what extent the disease is dangerous in their body. The proposed model is validated by numerical examples on real case studies. The experimental results indicate that the proposed system provides a viable solution that can work at wide range, a new platform to millions of people getting benefit over the decreasing of mortality and cost of clinical treatment related to heart failure. [ABSTRACT FROM AUTHOR]

Understanding is considered a key purpose of image forensic science in order to find out if a digital image is authenticated or not. It can be a sensitive task in case images are used as necessary proof as an impact judgment. it's known that There are several different manipulating attacks but, this copy move is considered as one of the most common and immediate one, in which a region is copied twice in order to give different information about the same scene, which can be considered as an issue of information integrity. The detection of this kind of manipulating has been recently handled using methods based on SIFT. SIFT characteristics are represented in the detection of image features and determining matched points. A clustering is a key step which always following SIFT matching in-order to classify similar matched points to clusters. The ability of the image forensic tool is represented in the assessment of the conversion that is applied between the two duplicated images of one region and located them correctly. Detecting copy-move forgery is not a new approach but using a new clustering approach which has been purposed by using the 2-level clustering strategy based on spatial and transformation domains and any previous information about the investigated image or the number of clusters need to be created is not necessary. Results from different data have been set, proving that the proposed method is able to individuate the altered areas, with high reliability and dealing with multiple cloning. [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]

The uncertainty principle is one of the most important features in modeling and solving linear time-invariant (LTI) systems. The neutrality phenomena of some factors in real models have been widely recognized by engineers and scientists. The convenience and flexibility of neutrosophic theory in the description and differentiation of uncertainty terms make it take advantage of modeling and designing of control systems. This paper deals with the controllability and stabilizability of LTI systems containing neutrosophic uncertainty in the sense of both indeterminacy parameters and functional relationships. We define some properties and operators between neutrosophic numbers via horizontal membership function of a relative-distance-measure variable. Results on exponential matrices of neutrosophic numbers are well-defined with the notion e tA deployed in a series of neutrosophic matrices. Moreover, we introduce the concepts of controllability and stabilizability of neutrosophic systems in the sense of Granular derivatives. Sufficient conditions to guarantee the controllability of neutrosophic LTI systems are established. Some numerical examples, related to RLC circuit and DC motor systems, are exhibited to illustrate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

Flower pollination algorithm (FPA) is a computational intelligence metaheuristic that takes its metaphor from flowers proliferation role in plants. This paper provides a comprehensive review of all issues related to FPA: biological inspiration, fundamentals, previous studies and comparisons, implementation, variants, hybrids, and applications. Besides, it makes a comparison between FPA and six different metaheuristics such as genetic algorithm, cuckoo search, grasshopper optimization algorithm, and others on solving a constrained engineering optimization problem. The experimental results are statistically analyzed with non-parametric Friedman test which indicates that FPA is superior more than other competitors in solving the given problem. [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]

In cloud computing environment, cross-cloud live migration of virtual machines (VMs) is a major concern in these days. Cloud computing provides the users with huge, versatile and on-demand access to a bulk of customizable and configurable registered physical devices or things. It helps organizations or enterprises to share data efficiently by privately owned cloud or by the third-party servers. This type of sharing of bulky data through cloud is more efficient and reliable. In an enterprise environment, one of the essential capabilities of cloud infrastructure is VM migration. VM live migration basically involves the transference of instances that includes the operating system, runtime memory pages and active CPU states from source hub to the destination hub. In this paper, we have discussed on resource allocation algorithm which performs better in utilization of CPU, time and memory. Our proposed algorithm deals with the effective utilization of unoccupied memory, and we have also measured VM memory stack flow of total memory for cloud computing architecture. [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]

Scheduling and managing projects are very important and hot topics in project management science. Multi-mode resource-constrained project scheduling problem (MM-RCPSP) is a RCPSP with special features in which each activity may be executed in more than one mode. Each mode has different options of cost, execution time, resources availabilities, and resources requirements. There are many well known measuring criteria related to the complexity and performance measures for scheduling projects, especially for the single mode projects. In this paper, two selection criteria for dealing with multi-mode resource constrained projects were suggested. According to these selection criteria, some well-known complexity and performance measures were modified for dealing with multi-mode projects. Five single-mode projects and five multi-mode projects are considered as test problems for applying the modified complexity and performance measures based on the suggested selection criteria. The obtained results rendered by the suggested selection criteria for test problems are compared by the existing criteria measures and the results are in the same trend and very promising. Also, we proposed new complexity measures and performance measures for MMRCP. The proposed complexity and performance measures also applied to the test problems. The obtained results rendered by the proposed complexity and performance measures are tested against the results obtained by existing complexity and performance measures. The new results are also promising and having the same trends. [ABSTRACT FROM AUTHOR]

Arabic Text document clustering is an important aspect for providing conjectural navigation and browsing techniques by organizing massive amounts of data into a small number of defined clusters. However, Words in form of vector are used for clustering methods is often unsatisfactory as it ignores relationships between important terms. Cluster analysis separates data into groups on clusters for the purposes of improved understanding or summarization. Clustering has a long history and many techniques developed in statistics, data mining, pattern recognition and other fields. This research proposes three approaches; Unsupervised, Semi Supervised techniques and Semi Supervised with dimensionality reduction to construct a clustering based classifier for Arabic text documents. Using k-means, incremental k-means, Threshold + k-means and k-means with dimensionality reduction, after document preprocessing removing stop words and gets the root for each term in each document. Then apply a term weighting method to get the weight of each term with respect to its document. Then apply a similarity measure method to each document and its similarity with other documents. And using F-measure, entropy and support vector machine (SVM) for calculate accuracy. The datasets are online dynamic datasets that are characterized by its availability and credibility on the internet. Arabic language is a challenging language when applied in an inference based algorithm. So, selecting the appropriate dataset is a principal factor in such research. The accuracy of those methods compared with other approaches and the proposed methods shows better accuracy and fewer errors for new classification test cases. Considering that the dimension reduction process is very sensitive because increasing the ratio of reduction can destroy important terms. [ABSTRACT FROM AUTHOR]

The Whale optimization algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social character of the humpback whales. WOA has an efficient performance in solving the continuous problems and engineering optimization problems. This paper presents an improved whale optimization algorithm (IWOA) for solving both single and multidimensional 0-1 knapsack problems with different scales. A penalty function is added to the evaluation function so that the fitness of the feasible solutions can outperform the fitness of the infeasible ones. The sigmoid function can take the real-valued solutions as input and produces the binary solutions as output. A two-stage repair operator is employed for handling the infeasible solutions. IWOA can give a better tradeoff between the diversification and the intensification through using two strategies: Local Search Strategy (LSS) and the Lévy flight walks. In addition to the bitwise operation which increases the IWOA efficiency. The proposed IWOA is compared with other state-of-art algorithms to validate the effectiveness of the proposed algorithm in solving 0-1 knapsack problems. Further, the experimental results and extensive numerical illustrations have been indicated that IWOA is efficient, effective, and robust for solving the hard 0-1 knapsack problems than the other existing approaches in the available literature. The source code of the IWOA algorithm will be available after the paper accepted as a toolbox in MATLAB library. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid krill herd (CSKH) approach to solve structural optimization problems. CSKH improved the Krill herd algorithm (KH) by combining KU/KA operator originated from cuckoo search algorithm (CS) with KH. In CSKH, a greedy selection scheme is used and often overtakes the original KH and CS. In addition, in order to further enhance the assessment of CSKH, a fraction of the worst krill is thrown away and substituted with newly randomly generated ones by KA operator at the end of each generation. The CSKH is applied to five real engineering problems to verify its performance. The experimental results have proven that CSKH algorithm is well capable of solving constrained engineering design problems more efficiently and effectively than the basic CS and KH algorithm. [ABSTRACT FROM AUTHOR]

This paper demonstrates our proposed Reuse Strategic Decision Pattern Framework (RSDPF) based on blending ANP and TOPSIS techniques, enabled by the OSM model with data analytics. The motivation, related work, theory, the use and deployment, and the service deployment of the framework have been discussed in details. In this paper, RSDPF framework is demonstrated by the data analysis and interpretations based on a financial service firm. The OSM model allows 3 step of processed to be performed in one go to perform statistical tests, identify linear relations, check consistency on dataset and calculate OLS regression. The aim is to identify the actual, expected and risk rates of profitability. Code and services can be reused to compute for analysis. Service integration of the RSDPF framework has been demonstrated. Results confirm that there is a high extent of reliability. In this paper, we have demonstrated the reuse and integration of the framework supported by the case study of the financial service firm with its data analysis and service to justify our research contributions – reuse and integration in statistical data mining, knowledge and heuristic discovery and finally domain transference. [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]

The main objective of this research is to study the integration of Analytic Hierarchy Process (AHP) into Delphi framework in neutrosophic environment and present a new technique for checking consistency and calculating consensus degree of expert’s opinions. In some pragmatism bearings, the experts might be not able to assign deterministic evaluation values to the comparison judgments due to his/her confined knowledge or the differences of individual judgments in group decision making. To overcome these challenges, we have used neutrosophic set theory to handle the integration of AHP into Delphi framework, where each pairwise comparison judgment is symbolized as a trapezoidal neutrosophic number. The power of AHP is enhanced by adding Delphi technique, since it can reduce noise which result from focusing on group and/or individual interests rather than concentriciting on problem disband and it also increase consensus degree about ideas. Obtaining a consistent trapezoidal neutrosophic preference relation is very difficult in decision making process because in creating a consistent trapezoidal preference relation; each expert should make n×n-12 consistent judgments for n alternatives. When the number of alternatives is increasing, the workload of giving judgments for the experts is heavy and it makes them tired and leads to inconsistent judgments. In the proposed model,experts will focus only on n-1 restricted judgments and this also enhances the performance of AHP over the traditional version that is proposed by Saaty. A real life example is developed based on expert opinions about evaluation process of many international search engines. The problem is solved to show the validation of the suggested method in neutrosophic path. [ABSTRACT FROM AUTHOR]

For any organization, the selection of suppliers is a very important step to increase productivity and profitability. Any organization or company seeks to use the best methodology and the appropriate technology to achieve its strategies and objectives. The present study employs the neutrosophic set for decision making and evaluation method (DEMATEL) to analyze and determine the factors influencing the selection of SCM suppliers. DEMATEL is considered a proactive approach to improve performance and achieve competitive advantages. This study applies the neutrosophic set Theory to adjust general judgment, using a new scale to present each value. A case study implementing the proposed methodology is presented (i.e. selecting the best supplier for a distribution company). This research was designed by neutrosophic DEMATEL data collection survey of experts, interviewing professionals in management, procurement and production. The results analyzed in our research prove that quality is the most influential criterion in the selection of suppliers. [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]

In this paper, a new modified version of the flower pollination algorithm based on the crossover for solving the multidimensional knapsack problems called (MFPA) is proposed. MFPA uses the sigmoid function as a discretization method to deal with the discrete search space. The penalty function is added to the evaluation function to recognize the infeasible solutions and assess them. A two-stage procedure is called FRIO is used to treat the infeasible solutions. MFPA uses an elimination procedure to decrease any duplication in the population in order to increase the diversity. The proposed algorithm is verified on a set of benchmark instances, and a comparison with other algorithms available in literature is shown. Several statistical and descriptive analysis was done such as recoding the results of the best, mean, worst, standard deviation, success rate, and time to prove the effectiveness and robustness of MFPA. The empirical results show that the proposed algorithm can be an effective algorithm as human-centric decision-making model for solving the multidimensional knapsack problems. [ABSTRACT FROM AUTHOR]

Nature-inspired metaheuristic algorithms are considered as the most effective techniques for solving various optimization problems. This paper provides a briefly review of the key features of the cuckoo-inspired metaheuristics: cuckoo search (CS) and cuckoo optimization algorithm (COA). In addition, it discusses some of their important and emerging studies, investigates their applications in several fields, and finally clarifies the differences between both algorithms so as to remove confusion between them. [ABSTRACT FROM AUTHOR]

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach. [ABSTRACT FROM AUTHOR]

The fourth paper titled I The Shortest Path Problem In Interval Valued Trapezoidal and Triangular Neutrosophic Environment i , by Broumi et al., proposed a new score function for interval-valued neutrosophic numbers and shortest path problem is solved using interval-valued neutrosophic numbers. Also, novel algorithms are proposed to find the neutrosophic shortest path by considering interval valued neutrosophic number, trapezoidal and triangular interval-valued neutrosophic numbers for the length of the path in a network with illustrative example. The sixth paper titled I Shortest Path Problem using Bellman Algorithm under Neutrosophic Environment i , by Broumi et al., proposed a novel algorithm to obtain the neutrosophic shortest path between each pair of nodes. [Extracted from the article]

Advances in the medical industry has become a major trend because of the new developments in information technologies. This research offers a novel approach for estimating the smart medical devices (SMDs) selection process in a group decision making (GDM) in a vague decision environment. The complexity of the selected decision criteria for the smart medical devices is a significant feature of this analysis. To simulate these processes, a methodology that combines neutrosophics using bipolar numbers with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under GDM is suggested. Neutrosophics with TOPSIS approach is applied in the decision making process to deal with the vagueness, incomplete data and the uncertainty, considering the decisions criteria in the data collected by the decision makers (DMs). In this research, the stress is placed upon the choosing of sugar analyzing smart medical devices for diabetics' patients. The main objective is to present the complications of the problem, raising interest among specialists in the healthcare industry and assessing smart medical devices under different evaluation criteria. The problem is formulated as a multi criteria decision type with seven alternatives and seven criteria, and then edited as a multi criteria decision model with seven alternatives and seven criteria. The results of the neutrosophics with TOPSIS model are analyzed, showing that the competence of the acquired results and the rankings are sufficiently stable. The results of the suggested method are also compared with the neutrosophic extensions AHP and MOORA models in order to validate and prove the acquired results. In addition, we used the SPSS program to check the stability of the variations in the rankings by the Spearman coefficient of correlation. The selection methodology is applied on a numerical case, to prove the validity of the suggested approach. [ABSTRACT FROM AUTHOR]

Supply chain (SC) networks now need to submit structural adjustments to respond to events like natural and unanticipated disasters that tend to cause interruptions and damage the continuity of business operations. Artificial intelligence, robotics, blockchain, 3D printing, 5G, and the Internet of Things are just a few examples of disruptive technologies that are being widely used and will drastically alter how SCs sustainability is conducted in the real world. The sustainability of SC operations can be greatly enhanced by disruptive technology. Organizational competency is needed to strike a balance between sustainability and SC disruption. Sustainable SCs may not have enough resilience to survive disruptive events since they prioritize efficiency and economic aspects. That is why sustainable SC management studies trends toward disruption utilizing new technologies. To adapt SCs to the needs of contemporary manufacturing processes, innovations in environmental conservation and social development have been put into place. In contrast, even though the fact that sustainability and disruptive technologies have each been extensively examined separately, few suggestions combine them in the measuring of SC performance. Since there are not any studies in the literature that identify the influence of disruptive technologies on SC sustainability generally, in this research we concentrate on several disruptive technologies that are used to provide SC sustainability. The objective of this work's suggestion is to provide a framework that integrates multi-criteria analysis methods with neutrosophic theory to evaluate the influence of disruptive technologies on the sustainability of SCs. This framework is an integration of the α-discounting technique for multi-criteria decision-making (α-D MCDM) and the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method. The new α-D MCDM approach overcomes the analytical hierarchy process's (AHP) method limitations of pairwise comparison to n-wise comparisons and also deals with inconsistent problems. SC sustainability has three main criteria which are economic, environmental, and social, their weights are measured using α-D MCDM with n-wise comparison. Also, the twenty sub-criteria weights were evaluated using the standard AHP method with consistent comparison. To rank the disruptive technologies that influence SC sustainability, we utilize the VIKOR method. The proposed framework is applied under a neutrosophic environment to ensure more accurate results and deal with uncertainty and vague conditions. Finally, sensitivity and comparative analyses were performed to confirm the verity, strength, and stability of the enhanced methodology. [ABSTRACT FROM AUTHOR]