Karam M Sallam

• PhD Computer Science
• Lecturer at Zagazig University

Mobile edge computing (MEC) servers integrated with multi-unmanned aerial vehicles (multi-UAVs) present a new system the multi-UAV-assisted MEC system. This system relies on the mobility of the UAVs to reduce the transmission distance between the servers and mobile users, thereby enhancing service quality and minimizing the overall energy consumpti...

This paper presents a binary variant of the recently proposed spider wasp optimizer (SWO), namely BSWO, for accurately tackling the multidimensional knapsack problem (MKP), which is classified as an NP-hard optimization problem. The classical methods could not achieve acceptable results for this problem in a reasonable amount of time. Therefore, th...

This study conducts a comparative analysis of the performance of ten novel and well-performing metaheuristic algorithms for parameter estimation of solar photovoltaic models. This optimization problem involves accurately identifying parameters that reflect the complex and nonlinear behaviours of photovoltaic cells affected by changing environmental...

This paper explores the transformative impact of the Internet of Medical Things (IoMT) on healthcare. By integrating medical equipment and sensors with the internet, IoMT enables real-time monitoring of patient health, remote patient care, and individualized treatment plans. IoMT significantly improves several healthcare domains, including managing...

Machine Learning (ML) with 5G technology has revolutionized smart healthcare. It has helped improve the quality of care, such as real-time analysis, decision-making, patient monitoring, and personalized treatments. In this paper, a 5G aware real-time diabetes prediction framework is proposed using optimized Bidirectional Long Short-Term Memory (Bi-...

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 loc...

This paper integrated the root assessment method (RAM) with a spherical fuzzy set (SFS) to rank the alternatives and select the best electricity production technology. The SF-Entropy is used to compute the factor’s weights, and the SF-RAM method ranks the alternatives. This study used four main factors, 29 subfactors, and 24 alternatives. The four...

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...

This study is presented to examine the performance of a newly proposed metaheuristic algorithm within discrete and continuous search spaces. Therefore, the multithresholding image segmentation problem and parameter estimation problem of both the proton exchange membrane fuel cell (PEMFC) and photovoltaic (PV) models, which have different search spa...

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...

Integrating the metaverse technology with the transportation system has several security and privacy issues. This study assesses the 12 security solutions to select the best one to overcome security and privacy issues (such as data theft, unauthorized access, and theft of personal data) when integrating the transportation system with metaverse tech...

Healthcare services must fulfill patients’ desires for secure data sharing and high accessibility. Blockchain technology, through blockchain platforms (BPs), can overcome healthcare challenges. This study develops a decision-making methodology for selecting the best BP, by integrating blockchain with IoT and Metaverse, the proposed approach ensures...

The advancement towards 6G networks necessitates innovative solutions for efficient deployment and management of heterogeneous networks. While the synergy of software-defined networks (SDN) and blockchain technology brings cost and spectrum efficiencies to future network deployments, their impact on network performance is yet to be investigated. Th...

There is an intensive need for intrusion detection systems (IDSs) due to incremental and frequent cyber-attacks. The first line of defense against online threats is an IDS. Researchers are using deep learning (DL) approaches to detect attackers and preserve user information. In this study, we introduce a hybrid DL-based model. The proposed model in...

Milk quality prediction is considered a vital research area due to increase the need for obtain sustainable development goals. This study aims to predict milk quality by integrate gated recurrent units (GRUs) and residual network (ResNet). Our model was evaluated on milk quality prediction dataset with seven unique feature such as pH, temperature,...

Finding a feasible path for an unmanned aerial vehicle (UAV) in a complex environment is a crucial part of any UAV mission planning system. Many algorithms have been developed to identify optimal or nearly optimal pathways for UAVs; however, the vast majority of those algorithms do not deal with this problem as multiobjective. Therefore, this study...

Ensuring Responsible AI practices is paramount in the advancement of systems founded upon machine learning (ML) principles, particularly in sensitive domains like intrusion detection within cybersecurity. A fundamental aspect of Responsible AI is reproducibility, which guarantees the reliability and transparency of research outcomes. In this paper,...

Binary optimization problems belong to the NP-hard class because their solutions are hard to find in a known time. The traditional techniques could not be applied to tackle those problems because the computational cost required by them increases exponentially with increasing the dimensions of the optimization problems. Therefore, over the last few...

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 op...

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...

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This r...

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 an...

This study presents a comprehensive solar hydrogen production plant assessment, focusing on evaluating its technological efficiency, economic viability, environmental impact, and operational reliability. Leveraging solar photovoltaic technology and electrolysis processes, the plant converts abundant solar energy into hydrogen, offering a sustainabl...

Bearings offers a variety of products designed to meet the rigorous requirements of the medical sector for patient safety, such as diagnostic and laboratory equipment, surgical and dental instruments, ventilators, heart pumps, etc. In the field of medical instruments, the bearing rings compromise from superior quality metals to perform surgical ope...

The intuitive fuzzy set has found important application in decision-making and machine learning. To enrich and utilize the intuitive fuzzy set, this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge. Retinal image detections are categorize...

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...

Slicing-enabled communication networks refer to a network architecture that enables the definition of multiple virtual networks or "slices" over a shared physical network. Each slice operates independently with its own dedicated resources, configuration, and management. However, this poses a major challenge in guaranteeing optimal resource allocati...

Industry 4.0 provides businesses with the tools they need to meet difficulties such as fluctuating demand and unstable markets. Additionally, Industry 4.0 refers to the connectivity of computers, various materials, and artificial intelligence (AI) with minimum involvement from humans in the decision-making process. Although Industry 4.0 has a signi...

Hybrid Active Power Filter (HAPF) imbibes the advantages of both passive and active power filters. These filters are considered one of the important technologies for mitigating harmonic pollution in electrical systems. Accurate estimation of filter parameters is a key component to reduce harmonic pollution effectively. In recent years, several opti...

The autonomous vehicle (AV) is one of the emerging technologies of the new age that has the potential to restructure transportation infrastructure. AVs are able to sense their surroundings and move around with control and self-sufficiency. AVs can contribute towards reducing traffic congestion on the roads, improving the quality of life, and achiev...

In this paper, a binary variant of a novel nature-inspired metaheuristic algorithm called the nutcracker optimization algorithm (NOA) is presented for binary optimization problems. Because of the continuous nature of the classical NOA and the discrete nature of the binary problems, two different families of transfer functions, namely S-shaped and V...

In the aftermath of the COVID-19 pandemic, the need for efficient and reliable disease diagnosis in smart cities has become increasingly serious. In this study, we introduce a novel blockchain-based federated learning framework tailored specifically for the diagnosis of pandemic diseases in smart cities, called BFLPD, with a focus on COVID-19 as a...

The emission crisis in the iron and steel sector prompted the search for modern systems that contribute to reducing the resulting emissions to alleviate the growing concerns about global warming. This research evaluates four flue gas treatment systems in the iron and steel sector through a case study in Egypt. A comprehensive approach is presented...

This paper investigates a wireless blockchain network with mobile edge computing in which Internet of Things (IoT) devices can behave as blockchain users (BUs). This blockchain network’s ultimate goal is to increase the overall profits of all BUs. Because not all BUs join in the mining process, using traditional swarm and evolution algorithms to so...

Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new...

The usage of fossil fuels is regarded as the generation of energy alternative towards electric vehicles (EVs) in third-world nations for a cleaner transportation sector. The rapid development of EVs is the most effective solution, even if the short-term ecological benefits for third-world nations cannot cover the short-term expenses. Since ecologic...

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...

Due to the extensive usage of fossil fuels such as coal, oil, and gas, the energy crisis and environmental pollution issues have garnered global attention, making the creation of clean, renewable energy an unavoidable option. Solar photovoltaic energy production is regarded as one of the most promising technologies owing to its safety, dependabilit...

Numerous studies in recent years have documented the negative effects of plastic waste on the environment and human wellness. Due to their widespread usage in daily life, particularly in packaging, and their rising direct or indirect discharge into the environment, plastics are recognized as an emerging environmental hazard. Thus, this point is con...

The conviction in the necessity of renewable energy has been prompted by both the constantly increasing need for power production and the ecological issues of recent years. When it comes to generating power in a sustainable manner, wind is among the most essential renewable energy sources. This research attempts to present a structural technique fo...

Quality evaluation is crucial to guaranteeing the dependability and uniformity of suppliers' products, components, and services. An overview of important topics concerning the evaluation of supplier quality is presented in this study. The evaluation includes everything from product quality and regulatory compliance to production methods, delivery t...

The advent of the Internet of Things (IoT) has ushered in a transformative era in supply chain management, revolutionizing the way organizations monitor, analyze, and optimize their operations. This comprehensive survey paper explores the multifaceted landscape of IoT applications in supply chain management, shedding light on the challenges, opport...

The knapsack problem (KP) is a discrete combinatorial optimization problem that has different utilities in many fields. It is described as a non-polynomial time (NP) problem and has several applications in many fields. The differential evolution (DE) algorithm has been successful in solving continuous optimization problems, but it needs further wor...

This paper presents a binary variant of a novel physics-based meta-heuristic optimization algorithm, namely Light spectrum optimizer (LSO), for tackling both the 0–1 knapsack (KP01) and multidimensional knapsack problems (MKP). Because of the continuous nature of the standard LSO that contradicts the knapsack problem's discrete nature, two various...

As a new attempt to design a precise mathematical model for the proton exchange membrane fuel cell (PEMFC), in this paper, three recently-proposed well-established optimizers: horse herding optimization algorithm (HOA), seagull optimization algorithm (SOA) and gradient-based optimizer (GBO) integrated with two newly-proposed effective strategies, n...

Deep neural network models can achieve greater performance in numerous machine learning tasks by raising the depth of the model and the amount of training data samples. However, these essential procedures will proportionally raise the cost of training deep neural network models. Accelerating the training process of deep neural network models in a d...

Recently, many metaheuristic optimization algorithms have been developed to address real-world issues. In this study, a new physics-based metaheuristic called Fick's law optimization (FLA) is presented, in which Fick's first rule of diffusion is utilized. According to Fick's law of diffusion, molecules tend to diffuse from higher to lower concentra...

Lung ultrasound images have shown great promise to be an operative point-of-care test for the diagnosis of COVID-19 because of the ease of procedure with negligible individual protection equipment, together with relaxed disinfection. Deep learning (DL) is a robust tool for modeling infection patterns from medical images; however, the existing COVID...

The parameter assessment of solar cells and photovoltaic (PV) modules is a challenging task due to the non-linearity behavior of the current–voltage (I–V) characteristic curve. This paper presents two hybrid nature-inspired algorithms for estimating the unknown parameters of the Single-Diode Model (SDM), and Double-Diode Model (DDM). These algorith...

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard prob...

This paper contains two main parts, Part I and Part II, which discuss the local and global minimization problems, respectively. In Part I, a fresh conjugate gradient (CG) technique is suggested and then combined with a line-search technique to obtain a globally convergent algorithm. The finite difference approximations approach is used to compute t...

To develop new meta-heuristic algorithms and evaluate on the benchmark functions is the most challenging task. In this paper, performance of the various developed meta-heuristic algorithms are evaluated on the recently developed CEC 2021 benchmark functions. The objective functions are parametrized by inclusion of the operators, such as bias, shift...

This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. I...

Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs with...

With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardises their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techni...

Cyber-attacks are getting increasingly complex, and as a result, the functional concerns of intrusion-detection systems (IDSs) are becoming increasingly difficult to resolve. The credibility of security services, such as privacy preservation, authenticity, and accessibility, may be jeopardized if breaches are not detected. Different organizations c...

Feature Selection (FS) is an important preprocessing step that is involved in machine learning and data mining tasks for preparing data (especially high-dimensional data) by eliminating irrelevant and redundant features, thus reducing the potential curse of dimensionality of a given large dataset. Consequently, FS is arguably a combinatorial NP-har...

Data mining applications are growing with the availability of large data; sometimes, handling large data is also a typical task. Segregation of the data for extracting useful information is inevitable for designing modern technologies. Considering this fact, the work proposes a chaos embed marine predator algorithm (CMPA) for feature selection. The...

The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine,...

Developing an appropriate model for accurate prediction of energy consumption is very essential for developing an effective energy management system for residential buildings. In view of this, the Short-term Load Forecasting (STLF) of household appliances has been performing an important role in supervising and managing energy in the residential co...

In this study, we introduce a new medical image enhancement approach depending on a type-2 neutrosophic set (T2NS) and α-mean and β- enhancement operations. This new approach obtains a good enhancement result by defining the uncertainties within the image in a six-degree membership. To show the real case study of this proposed technique, a novel en...