Segmenting medical images accurately is crucial for disease prevention and treatment. Despite the significant progress of deep learning techniques in semi-supervised segmentation, they still face the inability to effectively identify and utilize ambiguous regions with high predictive volatility in practical applications. Considering that ambiguous regions in unlabeled data contain more informative complementary cues, this article proposes an innovative ambiguous focusing and correction (AFoCo) framework. AFoCo consists of two parallel and complementary networks: the ambiguous focus and the ambiguous correction network. The ambiguous focus network combines historical change prediction and instantaneous information entropy to compute ambiguity indices and accurately capture ambiguous regions. Meanwhile, the ambiguous correction network utilizes the identified deterministic information to redistribute the pixel labels of the ambiguous region through the weight-weighted similarity strategy, thus effectively alleviating prediction volatility in ambiguous areas. Furthermore, we propose a task-aware asymmetric cross-supervision constraint, which assigns differentiated cross-pseudo supervision signals based on the task-specific characteristics of the two networks. By leveraging a consistency constraint, it enhances global prediction stability, ensuring precise ambiguous region focusing and high-quality feature rectification. The experimental results show that AFoCo performs better than other SOTA techniques on four medical image datasets, significantly improving the segmentation accuracy and effectively reducing the proportion of ambiguous regions.

This paper introduces Dynamic Social Particle Swarm Optimization (DS-PSO), a novel adaptation of the traditional Particle Swarm Optimization (PSO) technique specifically engineered for complex optimization challenges. DS-PSO innovatively incorporates dynamic social interactions within the swarm, enhancing adaptability and addressing the typical limitations of premature convergence and limited exploration in conventional PSO. A key feature of DS-PSO is its ability to balance exploration and exploitation efficiently, making it particularly suitable for dynamic environments. The primary application highlighted in this study is automatic clustering, a crucial task in data analysis involving unsupervised data grouping without prior knowledge of cluster numbers. DS-PSO’s flexibility and improved search capability demonstrate its potential as an effective tool for automatic clustering, promising significant advancements in data-driven optimization and analysis.

In recent years, ongoing advancements in the Industrial Internet of Things (IIoT) have yielded massive volumes of data, taxing the capabilities of cloud computing infrastructure. Allocating limited computing resources to numerous incoming requests is one of the difficulties of cloud computing, which is typically referred to as a Task-Scheduling-in-Cloud-Computing (TSCC) problem. In order to ameliorate the performance of a particle swarm optimizer (PSO) and broaden its application to TSCC, this paper introduces an Opposition-based Simulated Annealing Particle Swarm Optimizer (OSAPSO) to address PSO’s premature convergence issue, particularly when tackling high-dimensional complex problems like TSCC. OSAPSO is a novel combination of opposition-based learning (OBL), evolution strategy, simulated annealing (SA), and swarm intelligence. At its initial stage, the swarm is formed at random by using OBL to guarantee swarm diversity with a light computational burden. A multi-way tournament selection approach is then utilized to pick parents to produce a new offspring swarm by using two novel evolutionary operators, namely, damping-based mutation and inversion–scrambling-based crossover. OSAPSO is given a powerful exploration capacity by adopting the survivor probabilistic selection of SA, which accepts subpar solutions with a certain probability. Finally, PSO itself kicks in, making a good trade-off between solution diversity and convergence speed of the proposed method. Due to the non-convex discontinuous nature of TSCC, OSAPSO is modified to clone it into a discrete optimization problem. Within a heterogeneous cloud computing environment, OSAPSO and eight well-regarded competitors are examined on a set of multi-scale IIoT heterogeneous task groups (realistic and synthetic). In terms of power consumption, monetary cost, service makespan, and system throughput, experimental results reveal that OSAPSO has a winning performance and is statistically more significant in handling the challenge of IIoT task scheduling on two different replicated scenarios of cloud systems.

Smart cities rely mainly on the Internet of Things (IoT) to make an urban area smart to offer its citizens a high quality of life with optimal use of resources and preservation of the environment. IoT is the key component that collects raw data on the surrounding environment to be analyzed to extract information that supports decision making. The widespread use of IoT results in the emergence of smart homes, smart energy, smart transportation, and smart healthcare, which build a smart city. On the other hand, challenges, such as heterogeneity, scalability, security, and privacy, hinder the efficient functioning of the IoT in the construction of smart cities. This article presents a comprehensive overview on the concept of IoT moving forward to the concept of smart city, highlighting key elements and characteristics, studying and reviewing state-of-the-art research on this theme. Future directions are discussed to guide researchers, who focus on interoperability between IoT platforms in smart cities and on IoT architectures based on micro-services. Case studies of successful smart cities are presented for gaining learned lessons. The impact of integrating wireless networks (5G and 6G) in the IoT is also clarified in the future direction. The significance of this research is found in its comprehensive examination of various aspects of the smart city instead of concentrating on a singular facet.

Abstract This paper introduces a nonlinear PI controller for improved speed regulation in permanent magnet direct current (PMDC) motor drive systems. The nonlinearity comes from the exponential (Exp) block placed in front of the classical PI controller, which uses a tunable exponential function to map the speed error nonlinearly. Such a configuration has not been studied till now, thus meriting further investigation. We consider an exponential PI (EXP-PI) controller and to attain the best performance from this controller, its parameters are optimized offline using salp swarm algorithm (SSA), which borrows its inspiration from the way of forage and navigation of salps living in deep oceans. To indicate the credibility of SSA tuned EXP-PI controller convincingly, numerous experiments on speed regulation in PMDC motor have been implemented using DSP of TMS320F28335. The results obtained are also compared to similar results in the literature. It is shown that the proposed approach performs well in practice by ensuring tight tracking of the speed reference and superb torque disturbance rejection for the closed loop control. Furthermore, superior performance is achieved by the proposed nonlinear PI controller with respect to a fixed-gain PI controller.

Abstract Very sensitive loads require the safe operation of electrical distribution networks, including hospitals, nuclear and radiation installations, industries used by divers, etc. To address this issue, the provided paper suggests an innovative method for evaluating the appropriate allocation of Distribution STATic COMpensator (DSTATCOM) to alleviate total power losses, relieve voltage deviation, and lessen capital annual price in power distribution grids (PDGs). An innovative approach, known as the modified capuchin search algorithm (mCapSA), has been introduced for the first time, which is capable of addressing several issues regarding optimal DSTATCOM allocation. Furthermore, the analytic hierarchy process method approach is suggested to generate the most suitable weighting factors for the objective function. In order to verify the feasibility of the proposed mCapSA methodology and the performance of DSTATCOM, it has been tested on two standard buses, the 33-bus PDG and the 118-bus PDG, with a load modeling case study based on real measurements and analysis of the middle Egyptian power distribution grid. The proposed mCapSA technique's accuracy is evaluated by comparing it to other 7 recent optimization algorithms including the original CapSA. Furthermore, the Wilcoxon sign rank test is used to assess the significance of the results. Based on the simulation results, it has been demonstrated that optimal DSTATCOM allocation contributes greatly to the reduction of power loss, augmentation of the voltage profile, and reduction of total annual costs. As a result of optimized DSTATCOM allocation in PDGs, distribution-level uncertainties can also be reduced.

Abstract Emotion is an interdisciplinary research field investigated by many research areas such as psychology, philosophy, computing, and others. Emotions influence how we make decisions, plan, reason, and deal with various aspects. Automated human emotion recognition (AHER) is a critical research topic in Computer Science. It can be applied in many applications such as marketing, human–robot interaction, electronic games, E-learning, and many more. It is essential for any application requiring to know the emotional state of the person and act accordingly. The automated methods for recognizing emotions use many modalities such as facial expressions, written text, speech, and various biosignals such as the electroencephalograph, blood volume pulse, electrocardiogram, and others to recognize emotions. The signals can be used individually(uni-modal) or as a combination of more than one modality (multi-modal). Most of the work presented is in laboratory experiments and personalized models. Recent research is concerned about in the wild experiments and creating generic models. This study presents a comprehensive review and an evaluation of the state-of-the-art methods for AHER employing machine learning from a computer science perspective and directions for future research work.

Abstract The interest in incorporating environmentally friendly and renewable sources of energy, like photovoltaic (PV) technology, into electricity grids has grown significantly. These sources offer benefits, such as reduced power losses and improved voltage stability. To optimize these advantages, it is essential to determine optimal placement and management of these energy resources. This paper proposes an Improved RUNge–Kutta optimizer (IRUN) for allocating PV-based distributed generations (DGs) and Battery Energy Storage (BES) in distribution networks. IRUN utilizes three strategies to avoid local optima and enhance exploration and exploitation phases: a non-linear operator for smoother transitions, a Chaotic Local Search for thorough exploration, and diverse solution updates for refinement. The efficacy of IRUN is evaluated using 10 benchmark functions from the CEC’20 test suite, followed by statistical analysis. Next, IRUN is used to optimize the allocation of PVDG and BES to minimize energy losses in two standard IEEE distribution networks. The optimization problem is divided into two stages. In the first stage, the optimal size and the location of PV systems are calculated to meet peak load demand. In the second stage, considering time-varying load demand and intermittent PV generation, effective energy management of BES is employed. The effectiveness of IRUN is compared against the original RUN and other well-known optimization algorithms through simulation results. The comprehensive analysis demonstrates that IRUN outperforms the compared algorithms, making it a leading solution for optimizing PV distributed generation and BES allocation in distribution networks and the results show that the energy loss reduction reaches 63.54% and 68.19% when using PVand BES in IEEE 33-bus and IEEE 69 bus respectively.

Abstract Many real-world problems demand optimization, minimization of costs and maximization of profits, and meta-heuristic algorithms have proficiently proved their ability to achieve optimum results. This study proposes an alternative algorithm of Lévy Flight Distribution (LFD) by integrating Opposition-based learning (OBL) operator, termed LFD-OBL, for resolving intrinsic drawbacks of the canonical LFD. The proposed approach adopts OBL operator for catering search stagnancy to ensure faster convergence rate. We validate the usefulness of our approach through IEEE CEC’20 test suite, and compare results with original LFD and several other counterparts such as Moth-flame optimization, whale optimization algorithm, grasshopper optimisation algorithm, thermal exchange optimization, sine-cosine algorithm, artificial ecosystem-based optimization, Henry gas solubility optimization, and Harris’ hawks optimization. To further validate the efficiency of LFD-OBL, we apply it on parameters optimization of Solar Cell based on the Three-Diode Photovoltaic model. The qualitative and quantitative results of all the experiments performed in this study suggest superiority of the proposed method.

This paper proposes an enhanced version of Equilibrium Optimizer (EO) called (EEO) for solving global optimization and the optimal power flow (OPF) problems. The proposed EEO algorithm includes a new performance reinforcement strategy with the Lévy Flight mechanism. The algorithm addresses the shortcomings of the original Equilibrium Optimizer (EO) and aims to provide better solutions (than those provided by EO) to global optimization problems, especially OPF problems. The proposed EEO efficiency was confirmed by comparing its results on the ten functions of the CEC'20 test suite, to those of other algorithms, including high-performance algorithms, i.e., CMA-ES, IMODE, AGSK and LSHADE_cnEpSin. Moreover, the statistical significance of these results was validated by the Wilcoxon's rank-sum test. After that, the proposed EEO was applied to solve the the OPF problem. The OPF is formulated as a nonlinear optimization problem with conflicting objectives and subjected to both equality and inequality constraints. The performance of this technique is deliberated and evaluated on the standard IEEE 30-bus test system for different objectives. The obtained results of the proposed EEO algorithm is compared to the original EO algorithm and those obtained using other techniques mentioned in the literature. These Simulation results revealed that the proposed algorithm provides better optimized solutions than 20 published methods and results as well as the original EO algorithm. The EEO superiority was demonstrated through six different cases, that involved the minimization of different objectives: fuel cost, fuel cost with valve-point loading effect, emission, total active power losses, voltage deviation, and voltage instability. Also, the comparison results indicate that EEO algorithm can provide a robust, high-quality feasible solutions for different OPF problems.

Abstract This paper provides a novel, unique, and improved optimization algorithm called the modified Orca Predation Algorithm (mOPA). The mOPA is based on the original Orca Predation Algorithm (OPA), which combines two enhancing strategies: Lévy flight and opposition-based learning. The mOPA method is proposed to enhance search efficiency and avoid the limitations of the original OPA. This mOPA method sets up to solve the global optimization issues. Additionally, its effectiveness is compared with various well-known metaheuristic methods, and the CEC’20 test suite challenges are used to illustrate how well the mOPA performs. Case analysis demonstrates that the proposed mOPA method outperforms the benchmark regarding computational speed and yields substantially higher performance than other methods. The mOPA is applied to ensure that all load demand is met with high reliability and the lowest energy cost of an isolated hybrid system. The optimal size of this hybrid system is determined through simulation and analysis in order to service a tiny distant location in Egypt while reducing costs. Photovoltaic panels, biomass gasifier, and fuel cell units compose the majority of this hybrid system’s configuration. To confirm the mOPA technique’s superiority, its outcomes have been compared with the original OPA and other well-known metaheuristic algorithms.

Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model generalization. In addition to minimizing feature dimensionality, it also enhances classification accuracy and reduces model complexity, which are essential in several applications. Traditional methods for feature selection often fail in the optimal global solution due to the large search space. Many hybrid techniques have been proposed depending on merging several search strategies which have been used individually as a solution to the FS problem. This study proposes a modified hunger games search algorithm (mHGS), for solving optimization and FS problems. The main advantages of the proposed mHGS are to resolve the following drawbacks that have been raised in the original HGS; (1) avoiding the local search, (2) solving the problem of premature convergence, and (3) balancing between the exploitation and exploration phases. The mHGS has been evaluated by using the IEEE Congress on Evolutionary Computation 2020 (CEC'20) for optimization test and ten medical and chemical datasets. The data have dimensions up to 20000 features or more. The results of the proposed algorithm have been compared to a variety of well-known optimization methods, including improved multi-operator differential evolution algorithm (IMODE), gravitational search algorithm, grey wolf optimization, Harris Hawks optimization, whale optimization algorithm, slime mould algorithm and hunger search games search. The experimental results suggest that the proposed mHGS can generate effective search results without increasing the computational cost and improving the convergence speed. It has also improved the SVM classification performance.

This paper presents a novel intelligent system based on graph convolutional neural networks to study road crack detection in intelligent transportation systems. The visual features of the input images are first computed using the well-known Scale-Invariant Feature Transform (SIFT) extraction algorithm. Then, a correlation between SIFT features of similar images is analyzed and a series of graphs are generated. The graphs are trained on a graph convolutional neural network, and a hyper-optimization algorithm is developed to supervise the training process. A case study of road crack detection data is analyzed. The results show a clear superiority of the proposed framework over state-of-the-art solutions. In fact, the precision of the proposed solution exceeds 70%, while the precision of the baseline methods does not exceed 60%.

Abstract This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up‐to‐date medical learning models regarding both the computational and cost the quality of returned solutions.

Abstract The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi‐agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor‐based data. Multi‐agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state‐of‐the‐art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions.

Exploring the finest shortest-path traveling salesman optimization application is a typical NP-hard problem. Similarly the solution of the large-scale optimization applications is also a big challenging issue in front of scientists. First, African Vultures Optimization Algorithm (AVOA) was developed to resolve continuous applications where it performed fine. In the last few months, many enhanced strategies of AVOA have been offered in recent literature works and it has been extensively utilized to resolve large-scale engineering optimization applications. This study offers a newly modified dimension learning hunting (DLH)-based AVOA called DLHAV algorithm to resolve highly complex continuous and discrete applications. It helps improve the imbalance amid the hunting (or exploitation) and search (or exploration), the lack of crowd diversity, slow convergence speed, trapping in local optima, and early convergence of the AVOA variant. The proposed strategy benefits from a newly driven approach called the DLH search approach congenital from the separate exploitation behavior of vultures in the search domain. DLH exploration strategy utilizes a distinct method to make the best neighborhood for all vultures in which the nearest member information can be supplied amid vultures. DLH helps in improving the balance amid global and local and sustains diversity. To scrutinize the performance of DLHAV, the solutions of the DLHAV method are verified on 29-CEC'17 and 10-CEC'20 with familiar comparative methods and some other classical optimization approaches over many familiar traveling salesman problem/large-scale instances. With the intention of attaining unbiased and rigorous comparison, descriptive statistics such as standard deviation and mean have been applied, and the statistical Friedman test is also conducted. The experimental solution carried out in this study has revealed that the proposed algorithm outperforms significantly over the other alternative optimizers.

Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.

Abstract Affective computing, a subcategory of artificial intelligence, detects, processes, interprets, and mimics human emotions. Thanks to the continued advancement of portable non-invasive human sensor technologies, like brain–computer interfaces (BCI), emotion recognition has piqued the interest of academics from a variety of domains. Facial expressions, speech, behavior (gesture/posture), and physiological signals can all be used to identify human emotions. However, the first three may be ineffectual because people may hide their true emotions consciously or unconsciously (so-called social masking). Physiological signals can provide more accurate and objective emotion recognition. Electroencephalogram (EEG) signals respond in real time and are more sensitive to changes in affective states than peripheral neurophysiological signals. Thus, EEG signals can reveal important features of emotional states. Recently, several EEG-based BCI emotion recognition techniques have been developed. In addition, rapid advances in machine and deep learning have enabled machines or computers to understand, recognize, and analyze emotions. This study reviews emotion recognition methods that rely on multi-channel EEG signal-based BCIs and provides an overview of what has been accomplished in this area. It also provides an overview of the datasets and methods used to elicit emotional states. According to the usual emotional recognition pathway, we review various EEG feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods (e.g., convolutional and recurrent neural networks with long short term memory). In addition, EEG rhythms that are strongly linked to emotions as well as the relationship between distinct brain areas and emotions are discussed. We also discuss several human emotion recognition studies, published between 2015 and 2021, that use EEG data and compare different machine and deep learning algorithms. Finally, this review suggests several challenges and future research directions in the recognition and classification of human emotional states using EEG.

Evolution of the Internet of Things (IoT) makes a revolution in connecting, monitoring, controlling, and managing things, objects, and almost surroundings through the Internet. To reveal the potential of IoT, rich knowledge has to be extracted, indexed, and shared securely in real time. Recent comprehensive researches on IoT spot the light on main correlative challenges, such as security, scalability, heterogeneity, and big data. Due to the heterogeneity of IoT applications that produce a large volume of a variety of data streams in real time, mining, securing, and analyzing IoT data become tedious and challenging tasks. Indexing sensory data is one of data mining techniques, which ease information retrieval. But ordinary indexing methods are not fit with such massive and dynamic data; where indexes become out-of-date once they are built. Clustering, data reduction, and summarization present promising solutions for enabling low-power security and balanced indexing. This article presents a novel method for dynamic data reduction and summarization using dynamic time warping (DTW), which also presents a balanced architecture for enabling balanced indexing based on similarity data fusion. Data reduction-based prediction models enable real-time search and secure discovery for Smart Things (SThs). The results of the proposed model were proved using real examples and data sets. Using the Szeged-weather data set similar SThs data is reduced by 95%. Thus, indexes sizes could be reduced, and using smart scheduling, crawling cycle length could be expanded.

The security of data over the internet is a crucial thing specially if this data is personal or confidential.The transmitted data can be intercepted during its journey from device to another.For that reason, we are willing to develop a simple method to secure data.Data encryption is one method to secure the messages but the intruders can still try to crack it, in order to overcome this, steganography has been used to hide the data into a cover media(i.e.audio, image or video).Recently steganography attracts many researchers as a hot topic.This paper proposes an advanced technique for encrypting data using Advanced Encryption System (AES) and hiding the data using Haar Discreet Wavelet Transform (HDWT).HDWT aims to decrease the complexity in image steganology while providing less image distortion and lesser detectability.One-forth of the image carrying the details of the image in a region and other three regions carrying a less details of the image then the cipher text is concealed at most two Least Significant Bits (LSB) positions in the less detailed regions of the carrier image, if the message doesn't fit in the first LSB only it will use the second LSB.This proposed algorithm covers almost all type of symbols and alphabets.