My research focuses on Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Smart Systems, Bio-inspired Optimization, and intelligent engineering applications.
The publication list below is automatically synchronized with my Google Scholar public profile.
Calibrated adaptive framework for trustworthy human and artificial intelligence decision systems: M. AL-Ali et al.
The integration of artificial intelligence (AI) into safety-critical industrial decision-making promises substantial gains in efficiency and reliability, yet real-world deployment remains constrained by a deeper systemic problem: miscalibrated risk estimates, static trust assumptions, and unmodeled human cognitive biases jointly destabilize collaboration under operational pressure. In predictive maintenance and similar high-stakes settings, overconfident AI recommendations and…
A systematic review of inclusive intelligent transportation systems in smart cities
Ensuring equitable mobility for individuals with disabilities remains a critical yet under-addressed challenge in Intelligent Transportation Systems (ITS) and smart-city development. While prior ITS reviews have explored technological advancements and, in some cases, interdisciplinary perspectives, limited attention has been given to a comprehensive, accessibility-driven synthesis that integrates artificial intelligence (AI), policy frameworks, ethical considerations, datasets,…
Comprehensive Machine Learning Pipeline for Encrypted Traffic Classification with Bayesian Optimization and Explainability
Encrypted traffic classification is critical for modern cybersecurity, yet traditional approaches are ineffective and existing learning-based methods often struggle with noisy data, limited interpretability, and inefficient optimization. To address these challenges, this paper presents a comprehensive machine learning pipeline optimization framework enhanced with sequential model-based Bayesian optimization for robust and explainable encrypted traffic classification. The…
Smart Enforcement of Disability Parking: A Drone-Based License Plate Recognition and Staged Optimization Framework
Unauthorized occupation of parking spaces designated for individuals with disabilities remains a persistent challenge in urban environments, limiting accessibility and inclusive mobility. This paper proposes an integrated UAV-assisted enforcement framework that combines drone-based imaging, onboard license plate recognition (LPR), IoT connectivity, and a staged optimization strategy for energy-aware surveillance. The framework employs a two-phase approach:…
Detection of disturbances and cyber-attacks in smart grids using explainable machine learning
Modern power systems are subjected to natural disruptions and cyberattacks, both of which have the potential to have catastrophic consequences on the grid’s stability and security. Besides, due to the sophistication of cyber-physical threats, including techniques like false data injection and command tampering, comprehensive detection strategies to counter the vulnerabilities have become an absolute necessity….
Integrating machine learning and explainable AI for employee attrition prediction in HR analytics: M. AL-Ali et al.
Employee attrition poses significant challenges to organizations, impacting productivity, morale, and financial stability. Predicting attrition and understanding its underlying drivers are critical for implementing effective retention strategies. In this study, we propose a comprehensive framework that utilizes advanced machine learning techniques to predict employee attrition and job change likelihood. The framework integrates robust preprocessing pipelines,…
Revolutionizing breast cancer diagnosis: A computer-aided diagnosis framework with Vision Transformers for multistage histopathology-based classification
Diagnosing breast cancer from histopathological images remains a complex task due to the presence of intricate tissue structures and imaging artifacts. This study presents a robust artificial intelligence-based (AI) framework for enhancing breast cancer diagnosis through a multi-stage computer-aided diagnosis (CAD) system. Utilizing Vision Transformers (ViT), the proposed framework performs hierarchical classification, beginning with benign…
A hierarchical deep learning framework with doubly regularized loss for robust malware detection and family categorization
The rapid escalation of cyber threats and the sophistication of malware demand detection frameworks that are accurate, mathematically grounded, and interpretable. Traditional deep learning approaches often rely on standard loss formulations and empirical tuning, leaving challenges such as class imbalance, overfitting, and error propagation insufficiently addressed. This study proposes a hierarchical deep learning framework for…
A hierarchical deep learning framework with doubly regularized loss for robust malware detection and family categorization: S. Alsaedi et al.
The rapid escalation of cyber threats and the sophistication of malware demand detection frameworks that are accurate, mathematically grounded, and interpretable. Traditional deep learning approaches often rely on standard loss formulations and empirical tuning, leaving challenges such as class imbalance, overfitting, and error propagation insufficiently addressed. This study proposes a hierarchical deep learning framework for…
Securing IoT networks: a machine learning approach for detecting unusual traffic patterns
The exponential growth of the Internet of Things (IoT) poses substantial security challenges due to its heterogeneous, decentralized nature. This paper introduces a machine learning (ML) framework to enhance IoT network security by identifying and mitigating anomalous traffic patterns. The combination of the NBaIoT and UNSW-NB15 datasets enabled us to construct a complete environment for…
Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer Histopathology Diagnosis
Bladder cancer represents a significant health problem worldwide, with it being a major cause of death and characterized by frequent recurrences. Effective treatment hinges on early and accurate diagnosis; however, traditional methods are invasive, time-consuming, and subjective. In this research, we propose a transparent deep learning model based on the YOLOv11 structure to not only…
Revolutionizing breast cancer diagnosis
Diagnosing breast cancer from histopathological images remains a complex task due to the presence of intricate tissue structures and imaging artifacts. This study presents a robust artificial intelligence-based (AI) framework for enhancing breast cancer diagnosis through a multi-stage computer-aided diagnosis (CAD) system. Utilizing Vision Transformers (ViT), the proposed framework performs hierarchical classification, beginning with benign…
Prompt-Driven Multimodal Segmentation with Dynamic Fusion for Adaptive and Robust Medical Imaging with Applications to Cancer Diagnosis
Background/Objectives: Medical image segmentation is a crucial task for diagnosis, treatment planning, and monitoring of cancer; however, it remains one of the toughest nuts to crack for Artificial Intelligence (AI)-based clinical applications. Deep-learning models have shown near-perfect results for narrow tasks such as single-organ Computed Tomography (CT) segmentation. Still, they fail to deliver under practicality,…
Empowering cognitive disabilities in transit: an explainable, emotion-aware ITS framework
People with disabilities need ongoing support and a balanced lifestyle. Smart cities like NEOM are emerging worldwide. The Saudi government has implemented several disability accessibility programs in public spaces and transportation. This article addresses a critical yet often neglected challenge: accurately recognizing and interpreting facial emotions in individuals with cognitive disabilities to foster better social…
Deep learning for pathology: Yolov8 with eigencam for reliable colorectal cancer diagnostics
Colorectal cancer (CRC) is one of the most common causes of cancer-related deaths globally, making a timely and reliable diagnosis essential. Manual histopathology assessment, though clinically standard, is prone to observer variability, while existing computational approaches often trade accuracy for interpretability, limiting their clinical utility. This paper introduces a deep learning framework that couples the…
Bridging research gaps in breast cancer detection: An ensemble approach informed by bibliometric analysis
One of the leading causes of cancer-related death for women is still breast cancer, which highlights the importance of early and precise diagnosis techniques. Despite medical imaging and deep learning advances, current models fail to combine innovative object identification and classification methods, limiting their diagnostic performance. A bibliometric study of 1145 research articles identified key…
AI-driven prognostics in pediatric bone marrow transplantation: a CAD approach with Bayesian and PSO optimization
Bone marrow transplantation (BMT) is a critical treatment for various hematological diseases in children, offering a potential cure and significantly improving patient outcomes. However, the complexity of matching donors and recipients and predicting post-transplant complications presents significant challenges. In this context, machine learning (ML) and artificial intelligence (AI) serve essential functions in enhancing the analytical…
A hybrid YOLOv10—Faster R-CNN framework for mobility-aid detection and traffic optimization in disability-inclusive smart cities
Efficient transportation for individuals with mobility disabilities in smart cities remains a critical challenge: high-speed detectors such as YOLO sacrifice precision under occlusion or poor lighting. Accurate models like Faster R-CNN incur latencies exceeding 100 ms per frame and lack integrated routing for disabled users. To address these shortcomings, this study proposes a hybrid YOLOv10–Faster…
An autoencoder-based approach for feature engineering in cardiovascular disease prediction
Heart disease remains the leading cause of death worldwide, underscoring the urgent need for accurate, timely diagnosis to improve patient outcomes. Traditional diagnostic methods often struggle with accuracy, delayed intervention, and the complexity of analyzing diverse patient data. Moreover, existing machine learning approaches face challenges due to missing values, class imbalances, and limited features, all…
Integrating IoT, Green AI, and Big Data Analytics in Climate Change Mitigation and Adaptation: Sustainable Smart Healthcare Systems as a Case Study
The motivation for this chapter stems from the significant global events of the past 3 years, including pandemics and natural disasters, which have highlighted the fragility of global systems and the need for improved resilience in the face of such challenges. However, these events have also demonstrated the potential of modern technology to respond to these…
From Sensors to Insights: Interpretable Audio-Based Machine Learning for Real-Time Vehicle Fault and Emergency Sound Classification
Unrecognized mechanical faults and emergency sounds in vehicles can compromise safety, particularly for individuals with hearing impairments and in sound-insulated or autonomous driving environments. As intelligent transportation systems (ITSs) evolve, there is a growing need for inclusive, non-intrusive, and real-time diagnostic solutions that enhance situational awareness and accessibility. This study introduces an interpretable, sound-based machine…
Schrödinger optimizer: A quantum duality-driven metaheuristic for stochastic optimization and engineering challenges
This paper introduces the Schrödinger Optimizer (SRA), a new metaheuristic algorithm motivated by principles of quantum mechanics, specifically Schrödinger's equation and wave-particle duality. SRA possesses a twin update mechanism that balances probabilistic exploration and deterministic exploitation, facilitating effective navigation in high-dimensional, intricate search spaces. The algorithm was extensively tested on benchmark suites such as CEC…
Adaptive Traffic Light Management for Mobility and Accessibility in Smart Cities
Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and limiting inclusivity. To address these limitations, this paper proposes H-ATLM (a hybrid adaptive traffic lights management), a system utilizing the deep deterministic…
Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach
With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents…
Challenging the status quo: Why artificial intelligence models must go beyond accuracy in cervical cancer diagnosis
Cervical cancer is a significant health issue affecting women globally, with a high number of new cases and deaths reported each year. The disease is linked to HPV infection, but early detection through Pap smear tests can significantly increase performance. Deep learning techniques, particularly convolutional neural networks, transfer learning, generative adversarial networks, and attention mechanisms,…
ESARSA-MRFO-FS: optimizing manta-ray foraging optimizer using expected-sarsa reinforcement learning for features selection
Disease prediction with the help of computers has achieved significant progress in this area; however, it still requires a more accurate identification of each data feature. In the past few years, ML-based medical diagnoses have become increasingly dependent on the datasets rather than the algorithms. Consequently, the more features in a dataset, the more difficult…
A dual-feature framework for enhanced diagnosis of myeloproliferative neoplasm subtypes using artificial intelligence
Myeloproliferative neoplasms, particularly the Philadelphia chromosome-negative (Ph-negative) subtypes such as essential thrombocythemia, polycythemia vera, and primary myelofibrosis, present diagnostic challenges due to overlapping morphological features and clinical heterogeneity. Traditional diagnostic approaches, including imaging and histopathological analysis, are often limited by interobserver variability, delayed diagnosis, and subjective interpretations. To address these limitations, we propose a novel…
Advances in AI technology in healthcare
This Special Issue unites 11 innovative research papers that study artificial intelligence applications in the fields of bioengineering and healthcare. The research contained herein demonstrates how AI technology advances through its applications, which range from assistive technologies for disabled people to machine learning models that predict and diagnose diseases. The research studies included in this…
AOA-guided hyperparameter refinement for precise medical image segmentation
Medical image segmentation faces significant challenges, including the need for extensive annotated data, the impact of hyperparameters, and the limitations of traditional CNN models. Breast cancer (BC) and COVID-19 imaging, in particular, require precise segmentation for accurate diagnosis and treatment planning. This study introduces a novel framework that utilizes the Archimedes Optimization Algorithm (AOA) to…
Toward inclusive smart cities: Sound-based vehicle diagnostics, emergency signal recognition, and beyond
Sound-based early fault detection for vehicles is a critical yet underexplored area, particularly within Intelligent Transportation Systems (ITSs) for smart cities. Despite the clear necessity for sound-based diagnostic systems, the scarcity of specialized publicly available datasets presents a major challenge. This study addresses this gap by contributing in multiple dimensions. Firstly, it emphasizes the significance…
Transformative approaches in breast cancer detection: integrating transformers into computer-aided diagnosis for histopathological classification
Breast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide, necessitating advancements in diagnostic methodologies to improve early detection and treatment outcomes. This study proposes a novel twin-stream approach for histopathological image classification, utilizing both histopathologically inherited and vision-based features to enhance diagnostic precision. The first stream utilizes Virchow2, a deep learning…
YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems
In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology,…
Early detection of monkeypox: Analysis and optimization of pretrained deep learning models using the Sparrow Search Algorithm
The global spread of monkeypox across over 40 countries is a major health challenge. Its symptoms resemble those of chickenpox and measles, complicating early diagnosis. When PCR tests are unavailable, computational lesion detection offers a promising option. This research presents a non-invasive diagnostic method using the Sparrow Search Algorithm (SpaSA). SpaSA is applied to improve…
Emerging Artificial Intelligence Technologies for Neurological and Neuropsychiatric Research
The advent of artificial intelligence (AI) has revolutionized neurological and neuropsychiatric research, offering powerful and efficient tools to analyze complex, multiomodal, and vast datasets that were previously intractable. Recent advancement of integrated Machine learning (ML) and deep learning (DL) algorithms have enabled researchers to uncover patterns and novel insights, advancing our understanding of neural mechanisms…
Toward Robust Arabic Sign Language Recognition via Vision Transformers and Local Interpretable Model-agnostic Explanations Integration
People with severe or substantial hearing loss find it difficult to communicate with others. Poor communication can have a significant impact on the mental health of deaf people. For individuals who are deaf or hard of hearing, sign language (SL) is the major mode of communication in their daily life. Motivated by the need to…
SDN-based reliable emergency message routing schema using Digital Twins for adjusting beacon transmission in VANET
Digital Twin (DT) has revolutionized the contextualized digital environment. This advancement enables real-time monitoring and simulation of events, leading to more effective decision-making. In smart transportation, DT plays a crucial role in enhancing various aspects of road decision-making, including optimizing routing decisions for Emergency Message (EM) forwarding in Vehicular Ad hoc Networks (VANETs). In this…
Intelligent parcel delivery scheduling using truck-drones to cut down time and cost
In the evolving landscape of logistics, drone technology presents a solution to the challenges posed by traditional ground-based deliveries, such as traffic congestion and unforeseen road closures. This research addresses the Truck–Drone Delivery Problem (TDDP), wherein a truck collaborates with a drone, acting as a mobile charging and storage unit. Although the Traveling Salesman Problem…
Facial image analysis for automated suicide risk detection with deep neural networks: AEE Rashed et al.
Accurately assessing suicide risk is a critical concern in mental health care. Traditional methods, which often rely on self-reporting and clinical interviews, are limited by their subjective nature and may overlook non-verbal cues. This study introduces an innovative approach to suicide risk assessment using facial image analysis. The Suicidal Visual Indicators Prediction (SVIP) Framework leverages…
An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network
Brain tumors must be classified to determine their severity and appropriate therapy. Applying Artificial Intelligence to medical imaging has enabled remarkable developments. The presented framework classifies patients with brain tumors with high accuracy and efficiency using CNN, pre-trained models, and the Manta Ray Foraging Optimization (MRFO) algorithm on X-ray and MRI images. Additionally, the CNN…
CardioRiskNet: A hybrid AI-based model for explainable risk prediction and prognosis in cardiovascular disease
The global prevalence of cardiovascular diseases (CVDs) as a leading cause of death highlights the imperative need for refined risk assessment and prognostication methods. The traditional approaches, including the Framingham Risk Score, blood tests, imaging techniques, and clinical assessments, although widely utilized, are hindered by limitations such as a lack of precision, the reliance on…
Economically optimized heat exchanger design: a synergistic approach using differential evolution and equilibrium optimizer within an evolutionary algorithm framework
This study introduces the CP-EODE algorithm, a novel hybrid of the Equilibrium Optimizer (EO), and the Differential Evolution (DE) algorithm. It addresses EO’s tendency toward premature convergence by enhancing its exploration capabilities. The motivation for this research stems from the need for more efficient and economically viable designs in engineering, particularly in the optimization of…
SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system
In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from…
Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication: H. ZainEldin et al.
People who often communicate via sign language are essential to our society and significantly contribute. They struggle with communication mostly because other people, who often do not understand sign language, cannot interact with them. It is necessary to develop a dependable system for automatic sign language recognition. This paper aims to provide a comprehensive review…
A novel multi-scaled deep convolutional structure for punctilious human gait authentication
The need for non-interactive human recognition systems to ensure safe isolation between users and biometric equipment has been exposed by the COVID-19 pandemic. This study introduces a novel Multi-Scaled Deep Convolutional Structure for Punctilious Human Gait Authentication (MSDCS-PHGA). The proposed MSDCS-PHGA involves segmenting, preprocessing, and resizing silhouette images into three scales. Gait features are extracted…
Advancing feature ranking with hybrid feature ranking weighted majority model: a weighted majority voting strategy enhanced by the Harris hawks optimizer
Feature selection (FS) is vital in improving the performance of machine learning (ML) algorithms. Despite its importance, identifying the most important features remains challenging, highlighting the need for advanced optimization techniques. In this study, we propose a novel hybrid feature ranking technique called the Hybrid Feature Ranking Weighted Majority Model (HFRWM2). HFRWM2 combines ML models…
Active convolutional neural networks sign language (ActiveCNN-SL) framework: a paradigm shift in deaf-mute communication
Real-time speech-to-text and text-to-speech technologies have significantly influenced the accessibility of communication for individuals who are deaf or mute. This research aims to assess the efficacy of these technologies in facilitating communication between deaf or mute individuals and those who are neither deaf nor mute. A mixed-method approach will incorporate qualitative and quantitative data collection…
Comprehensive analysis of digital twins in smart cities: a 4200-paper bibliometric study: RF El-Agamy et al.
This survey paper comprehensively reviews Digital Twin (DT) technology, a virtual representation of a physical object or system, pivotal in Smart Cities for enhanced urban management. It explores DT's integration with Machine Learning for predictive analysis, IoT for real-time data, and its significant role in Smart City development. Addressing the gap in existing literature, this…
Charting new frontiers: insights and future directions in ML and DL for image processing
The Special Issue “Deep and Machine Learning for Image Processing: Medical and Non-medical Applications” of the MDPI journal Electronics marks a pivotal point in the exploration of machine learning (ML) and deep learning (DL) applications in image processing. This Special Issue has received numerous submissions, totaling 35 papers, not including this Editorial paper, of which…
Assessing the accuracy and efficiency of kinematic analysis tools for six-DOF industrial manipulators: The KUKA robot case study
Accuracy is an important factor to consider when evaluating the performance of a manipulator. The accuracy of a manipulator is determined by its ability to accurately move and position objects in a precise manner. This research paper aims to evaluate the performance of different methods for the kinematic analysis of manipulators. The study employs four…
Toward interpretable credit scoring: integrating explainable artificial intelligence with deep learning for credit card default prediction
In recent years, the increasing prevalence of credit card usage has raised concerns about accurately predicting and managing credit card defaults. While machine learning and deep learning methods have shown promising results in default prediction, the black-box nature of these models often limits their interpretability and practical adoption. This study presents a new method for…
Quadratic interpolation and a new local search approach to improve particle swarm optimization: Solar photovoltaic parameter estimation
The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called…
Optimizing multi-layer perovskite solar cell dynamic models with hysteresis consideration using artificial rabbits optimization
Perovskite solar cells (PSCs) exhibit hysteresis in their J-V characteristics, complicating the identification of appropriate electrical models and the determination of the maximum power point. Given the rising prominence of PSCs due to their potential for superior performance, there is a pressing need to address this challenge. Existing solutions in the literature have not fully…
New diagnostic perspectives in urogenital radiology
Urogenital Radiology, a key area of medical imaging, focuses on diagnosing and treating urinary and reproductive system conditions. Its relevance has surged due to the rise in global urogenital disorders. The field has evolved with the adoption of advanced diagnostic tools like ultrasound, CT scans, and MRI. Ultrasound offers non-invasive, real-time imaging, while CT scans…
ASD2-TL∗ GTO: Autism spectrum disorders detection via transfer learning with gorilla troops optimizer framework
Autism Spectrum Disorder (ASD) treatment requires accurate diagnosis and effective rehabilitation. Artificial intelligence (AI) techniques in medical diagnosis and rehabilitation can aid doctors in detecting a wide range of diseases more effectively. Nevertheless, due to its highly heterogeneous symptoms and complicated nature, ASD diagnostics continues to be a challenge for researchers. This study introduces an…
Revolutionizing oral cancer detection: an approach using aquila and gorilla algorithms optimized transfer learning-based cnns
The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification…
Mathematical modeling and analysis of credit scoring using the lime explainer: a comprehensive approach
Credit scoring models serve as pivotal instruments for lenders and financial institutions, facilitating the assessment of creditworthiness. Traditional models, while instrumental, grapple with challenges related to efficiency and subjectivity. The advent of machine learning heralds a transformative era, offering data-driven solutions that transcend these limitations. This research delves into a comprehensive analysis of various machine…
A mathematical model for customer segmentation leveraging deep learning, explainable AI, and RFM analysis in targeted marketing
In the evolving landscape of targeted marketing, integrating deep learning (DL) and explainable AI (XAI) offers a promising avenue for enhanced customer segmentation. This paper introduces a groundbreaking approach, DeepLimeSeg, which synergizes DL methodologies with Lime-based Explainability to segment customers effectively. The approach employs a comprehensive mathematical model to harness demographic data, behavioral patterns, and…
Enhancing feature selection with GMSMFO: A global optimization algorithm for machine learning with application to intrusion detection
The paper addresses the limitations of the Moth-Flame Optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. The MFO algorithm, which employs moths' transverse orientation navigation technique, has been used to generate solutions for such problems. However, the performance of MFO is dependent on the flame production and spiral search components, and the search…
An innovative time-varying particle swarm-based Salp algorithm for intrusion detection system and large-scale global optimization problems
Particle swarm optimization (PSO) suffers from delayed convergence and stagnation in the local optimal solution, as do most meta-heuristic algorithms. This study proposes a time-based leadership particle swarm-based Salp (TPSOSA) to address the PSO's limitations. The TPSOSA is a novel search technique that addresses population diversity, an imbalance between exploitation and exploration, and the premature…
Deep learning based on LSTM model for enhanced visual odometry navigation system
UAVs are employed for military, commercial, environmental, and other objectives. Flying in complex situations might strain the GPS (GNSS). Using INS alone increases positional inaccuracy. Despite cameras and sensors, drift persists. This work provides a GNSS-free UAV navigation system employing optical odometry, radar height estimates, and multi-sensory data fusion. Our monocular VO with optical flow…
A two-stage renal disease classification based on transfer learning with hyperparameters optimization
Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which affect anywhere from 1 to 15% of the global population and thus; considered one of the leading causes of chronic kidney diseases (CKD). In addition to kidney stones, renal cancer is the tenth most prevalent…
An integrated machine learning-based brain computer interface to classify diverse limb motor tasks: Explainable model
Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI…
Large-scale competitive learning-based salp swarm for global optimization and solving constrained mechanical and engineering design problems
The Competitive Swarm Optimizer (CSO) has emerged as a prominent technique for solving intricate optimization problems by updating only half of the population in each iteration. Despite its effectiveness, the CSO algorithm often exhibits a slow convergence rate and a tendency to become trapped in local optimal solutions, as is common among metaheuristic algorithms. To…
Photovoltaic parameter estimation using improved moth flame algorithms with local escape operators
Optimizing, regulating, and simulating photovoltaic systems are crucial for producing solar energy. The performance of PV systems is greatly affected by model parameters, which can be variable and not always easily accessible. As a result, finding these model parameters is a constant goal. Current-voltage data is needed to extract characteristics of solar modules and construct…
I2OT-EC: A Framework for Smart Real-Time Monitoring and Controlling Crude Oil Production Exploiting IIOT and Edge Computing
The oil and gas business has high operating costs and frequently has significant difficulties due to asset, process, and operational failures. Remote monitoring and management of the oil field operations are essential to ensure efficiency and safety. Oil field operations often use SCADA or wireless sensor network (WSN)-based monitoring and control systems; both have numerous…
An improved parallel processing-based strawberry optimization algorithm for drone placement
It is challenging to place drones in the best possible locations to monitor all sensor targets while keeping the number of drones to a minimum. Strawberry optimization (SBA) has been demonstrated to be more effective and superior to current methods in evaluating engineering functions in various engineering problems. Because the SBA is a new method,…
An optimized quadratic support vector machine for eeg based brain computer interface
The Brain Computer Interface (BCI) has a great impact on mankind. Many researchers have been trying to employ different classifiers to figure out the human brain's thoughts accurately. In order to overcome the poor performance of a single classifier, some researchers used a combined classifier. Others delete redundant information in some channels before applying the…
Trends in smart healthcare systems for smart cities applications
Consider the most important lessons learned from the global achievements and disappointments of the previous year. It was a year filled with pandemics that exacerbated massive geopolitical, social, and economic shocks on a worldwide scale, bringing out the worst and best in people. However, the past two years have demonstrated the fragility of global institutions…
An improved optimally designed fuzzy logic-based MPPT method for maximizing energy extraction of PEMFC in green buildings
Recently, the concept of green building has become popular, and various renewable energy systems have been integrated into green buildings. In particular, the application range of fuel cells (FCs) has become widespread due to the various government plans regarding green hydrogen energy systems. In particular, proton exchange membrane fuel cells (PEMFCs) have proven superiority over…
M²BRTPC: A Novel Modified Multimodal Biometric Recognition for Toddlers and Pre-School Children Approach
A biometric system based on the characteristics of adults recently achieved an outstanding result. Over the last few decades, many applications have been developed for adults, such as fingerprint, face, iris, and hand-vein. Infant identification suffers from many problems and does not enroll all possible ages. Identifying children using one or any biometric features is…
Smart Bagged Tree-based Classifier optimized by Random Forests (SBT-RF) to Classify Brain-Machine Interface Data
Brain-Computer Interface (BCI) is a new technology that uses electrodes and sensors to connect machines and computers with the human brain to improve a person's mental performance. Also, human intentions and thoughts are analyzed and recognized using BCI, which is then translated into Electroencephalogram (EEG) signals. However, certain brain signals may contain redundant information, making…
Addressing constrained engineering problems and feature selection with a time-based leadership salp-based algorithm with competitive learning
Like most metaheuristic algorithms, salp swarm algorithm (SSA) suffers from slow convergence and stagnation in the local optima. The study develops a novel Time-Based Leadership Salp-Based Competitive Learning (TBLSBCL) to address the SSA’s flaws. The TBLSBCL presents a novel search technique to address population diversity, an imbalance between exploitation and exploration, and the SSA algorithm’s…
Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluid
This research work intends to enhance the stepped double-slope solar still performance through an experimental assessment of combining linen wicks and cobalt oxide nanoparticles to the stepped double-slope solar still to improve the water evaporation and water production. The results illustrated that the cotton wicks and cobalt oxide (Co3O4) nanofluid with 1wt% increased the hourly…
A honey badger optimization for minimizing the pollutant environmental emissions-based economic dispatch model integrating combined heat and power units
Traditionally, the Economic Dispatch Model (EDM) integrating Combined Heat and Power (CHP) units aims to reduce fuel costs by managing power-only, CHP, and heat-only units. Today, reducing pollutant emissions to the environment is of paramount concern. This research presents a novel honey badger optimization algorithm (HBOA) for EDM-integrated CHP units. HBOA is a novel meta-heuristic…
An innovative quadratic interpolation salp swarm-based local escape operator for large-scale global optimization problems and feature selection
Salp swarm algorithm (SSA) is a unique swarm intelligent algorithm widely used for various practical applications due to its simple framework and good optimization performance. However, like other swarm-based algorithms, SSA yields local optimal solutions and has a slow convergence rate and low solution accuracy when dealing with high-dimensional global optimization problems. Based on quadratic…
Genetic algorithm for the design and optimization of a shell and tube heat exchanger from a performance point of view
A new approach to optimize the design of a shell and tube heat exchanger (STHX) is developed via a genetic algorithm (GA) to get the optimal configuration from a performance point of view. The objective is to develop and test a model for optimizing the early design stage of the STHX and solve the design…
A discrete variant of cuckoo search algorithm to solve the Travelling Salesman Problem and path planning for autonomous trolley inside warehouse
Recently, order picking routing (OPR) for robots inside modern warehouses have become one of the most challenging problems. The process of OPR can be formulated as a Travelling Salesman Problem (TSP). Traditional techniques used to solve this problem usually require a long execution time and are problem-specific. Meta-heuristic optimisation techniques have been applied to solve…
Comparing SSALEO as a scalable large scale global optimization algorithm to high-performance algorithms for real-world constrained optimization benchmark
The Salp Swarm Algorithm (SSA) outperforms well-known algorithms such as particle swarm optimizers and grey wolf optimizers in complex optimization challenges. However, like most meta-heuristic algorithms, SSA suffers from slow convergence and stagnation in the best local solution. In this study, a Salp swarm algorithm (SSA) is combined with a local escaping operator (LEO) to…
An optimized deep learning approach for suicide detection through Arabic tweets
Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including…
Classification of breast cancer using a manta-ray foraging optimized transfer learning framework
Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology…
A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer
Alzheimer’s disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer’s disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and…
Large scale salp-based grey wolf optimization for feature selection and global optimization
Salp swarm algorithm (SSA) is a recently developed meta-heuristic swarm intelligence optimization algorithm based on simulating the chain movement behavior of salps sailing and foraging in the sea. In this paper, a novel hybrid meta-heuristic algorithm called SSA-FGWO is proposed to overcome the shortcomings of the original SSA, including slow convergence speed in dealing with…
An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network
Researchers have developed more intelligent, highly responsive, and efficient detection methods owing to the COVID-19 demands for more widespread diagnosis. The work done deals with developing an AI-based framework that can help radiologists and other healthcare professionals diagnose COVID-19 cases at a high level of accuracy. However, in the absence of publicly available CT datasets,…
Heat recovery steam generator (HRSG) three-element drum level control utilizing Fractional order PID and fuzzy controllers
Shrink and swell is a phenomenon that causes transient variability in water level once boiler load variation occurs. The leading cause of the swell effect is the steam demand changes and the actual arrangement of steam generating tubes in the boiler. Steam bubbles beneath HRSG drum water make the level control very difficult, particularly with…
Parameter identification of photovoltaic cell model using modified elephant herding optimization-based algorithms
The use of metaheuristics in estimating the exact parameters of solar cell systems contributes greatly to performance improvement. The nonlinear electrical model of the solar cell has some parameters whose values are necessary to design photovoltaic (PV) systems accurately. The metaheuristic algorithms used to determine solar cell parameters have achieved remarkable success; however, most of…
A novel cuckoo search algorithm with adaptive discovery probability based on double Mersenne numbers
Cuckoo search algorithm is one of the most prominent meta-heuristic optimization algorithms which is applied to various applications. The discovery probability is the one and the only tuning parameter of the cuckoo search algorithm. The physical meaning of this parameter contradicts its implementation in the standard algorithm. Therefore, this study concerns the correction to the…
Brain Strategy Algorithm for Multiple Object Tracking Based on Merging Semantic Attributes and Appearance Features
The human brain can effortlessly perform vision processes using the visual system, which helps solve multi-object tracking (MOT) problems. However, few algorithms simulate human strategies for solving MOT. Therefore, devising a method that simulates human activity in vision has become a good choice for improving MOT results, especially occlusion. Eight brain strategies have been studied…
Brain cancer prediction based on novel interpretable ensemble gene selection algorithm and classifier
The growth of abnormal cells in the brain causes human brain tumors. Identifying the type of tumor is crucial for the prognosis and treatment of the patient. Data from cancer microarrays typically include fewer samples with many gene expression levels as features, reflecting the curse of dimensionality and making classifying data from microarrays challenging. In…
Solid Oxide Fuel Cell Modeling Using Numerical Method and Neural Network.
Modeling Solid Oxide Fuel cell SOFC numerically and by AI-based technique is the main objective of this paper. Testing the reliability of a two dimensional numerical model of SOFC using COMSOL (FEMLAB 3.1) software against neural network model is another important issue that will be considered in this paper. In the proposed study, two layers…
Ambient healthcare approach with hybrid whale optimization algorithm and Naïve Bayes classifier
There is a crucial need to process patient’s data immediately to make a sound decision rapidly; this data has a very large size and excessive features. Recently, many cloud-based IoT healthcare systems are proposed in the literature. However, there are still several challenges associated with the processing time and overall system efficiency concerning big healthcare…
Induction Motor Drive Using Fractional-Order Proportional Integral Derivative (FOPID) Controller Based on Nelder-Mead and Grey Wolf Optimizers.
Induction motors are widely used in industrial applications due to their advantages over dc motors in terms of low cost, low maintenance, high performance, and high power density. This article aims to achieve constant speed control of the induction motor (IM) and improves the motor performance using a Fractional-order PID controller (FOPID). The FOPID controller…
ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound
Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public’s concern by now is to…
A fire detection model based on power-aware scheduling for IoT-sensors in smart cities with partial coverage: M. El-Hosseini et al.
Fire detection techniques have received considerable critical attention over the past ten years. Regardless of the progress in the area of fire detection, questions have been raised about the cost, complexity, consumed power from a large number of sensors to analyze sensors’ data. Debate continues about the best strategies for the management of consumed power…
Induction Motor Drive Using Fractional-Order Proportional Integral Derivative (FOPID) Controller Based on Nelder-Mead and Grey Wolf Optimizers.(Dept. E)
Revised:(08 June, 2021) Accepted:(10 June, 2021) Corresponding Author: Mohamed Said Essa, researcher at Faculty of Engineering, Mansoura University, Electrical Engineering,(email:[email protected]). Prof. Dr. Mostafa A. Elhosseini, professor at Department of Computers and Systems Engineering at Faculty of Engineering, Mansoura University,(email:[email protected]) Assoc. Prof. Dr. Eid Abdelbaki Gouda, associate professor at Electrical Engineering Department, Faculty of…
Heat Exchanger Design using Differential Evolution-Based ABC.
The main purpose of this work is to develop a cost-effective design of the shell and tube heat exchanger (STHE). The STHE objective function to be minimized is the total cost of STHE, which is a function of the surface area of the heat transfer and pressure drop at both tube and shell side. Artificial…
An improved dynamic deployment technique based-on genetic algorithm (IDDT-GA) for maximizing coverage in wireless sensor networks: H. ZainEldin et al.
Recently, many researchers have paid attention to wireless sensor networks (WSNs) due to their ability to encourage the innovation of the IT industry. Although WSN provides dynamically scalable solutions with various smart applications, the growing need to maximize the area coverage with decreasing the percentage of deployed sensor nodes is still required. Random deployment is…
Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches
Nowadays, a significant number of infectious diseases such as human coronavirus disease (COVID-19) are threatening the world by spreading at an alarming rate. Some of the literatures pointed out that the pandemic is exhibiting seasonal patterns in its spread, incidence and nature of the distribution. In connection to the spread and distribution of the infection,…
A harmony search-based H-infinity control method for islanded microgrid
This paper proposes a harmony search (HS) based H-infinity (H ∞ ) control method to promote the conventional droop control method. The proposed method is used to enhance the performance of the voltage/frequency (V/F), controller. It can regulate both voltage and frequency to their rated values while enhancing autonomous microgrid (MG) power quality. The results…
Performance validation of jaya algorithm to the most well-known testbench problem
Soft computing algorithms are population-based, probabilistic, that have the same common controlling parameters such as population size, number of generations, elite size. In addition to the regular control parameters, the different algorithms need specific control parameters for their algorithm. A good tuning of different algorithm parameters is an integral factor that affects the efficacy of…
MATLAB-based framework for data analytics applied to Hajj dataset: Hajj health meter
The total number of pilgrims for the Hajj Season of 1438H reached 2,352, 122 — according to the General Authority for statistics Kingdom of Saudi Arabia. Pilgrims data analysis and prediction help concerned entities of the country in the future planning programs for the purpose of ensuring the necessary services — social, health, security, food…
Robust Control Technique in an Autonomous Microgrid: A Multi-stage Controller Based on Harmony Search Algorithm
Microgrid (MG) control in off-grid mode of operation is one of the main challenges in MGs. Under different loading conditions, voltage and frequency deviate from their nominal limits. The droop control method that applied with the current control and voltage control loops can restore the MG voltage and frequency to their nominal limits. However, it…
Big Data, Cloud Computing, and IoT (BCI) Amalgamation Model: The Art of “Reinventing Yourself” to Analysis the World in Which We Live
The spread of omnipresent sensing technology brings with it an increasing number of innovative models. The smart mobility initiatives offer new opportunities for Intelligent Systems to maximize the utilization of real-time data that are streaming out of different sensory resources. In recent years, the convergence trend of Big Data, Cloud and IoT has received considerable…
Identification and speed control of dc motor using fractional order pid: Microcontroller
This paper uses Fractional-order PID control (FOPID) to control the speed of the DC motor. FOPID is more flexible and confident in controlling control higher-order systems compared to classical PID. In this work, the FOPID controller tuning is carried out using different methods ranging from classical techniques to most recent heuristic methods are Fractional Grey…
An innovative damped cuckoo search algorithm with a comparative study against other adaptive variants
This paper aims to find the best variant of Cuckoo Search Algorithm that makes the step size of Lévy flight adaptive. For this reason, we introduce a new variant of CSA called Damped Cuckoo Search (DCS) in which the step size of Lévy flight is adaptive via the concept of damped oscillations that exist in…
Culture-based artificial bee colony with heritage mechanism for optimization of wireless sensors network
In this paper, a hybridization model based on culture algorithm and Artificial Bee Colony is proposed. The objective of the hybrid model is mainly to get benefit of the previous knowledge gained by predecessor foragers which help bees searching for food sources in potential positions. The proposed CB-ABC focuses on the kind of information in…
Deployment techniques in wireless sensor networks, coverage and connectivity: A survey
Wireless sensor networks (WSNs) have gained wide attention from researchers in the last few years because it has a vital role in countless applications. The main function of WSN is to process extracted data and to transmit it to remote locations. A large number of sensor nodes are deployed in the monitoring area. Therefore, deploying…
On the performance improvement of elephant herding optimization algorithm
Thanks to fewer numbers of control parameters and easier implementation, the Elephant Herding Optimization (EHO) has been gaining research interest during the past decade. In our paper, to understand the impact of the control parameters, a parametric study of the EHO is carried out using a standard test bench, engineering problems, and real-world problems. On…
Biped robot stability based on an A–C parametric whale optimization algorithm
The easy gait stability of biped robot is an important issue and has been mentioned in different works in literature. To evaluate the walking stability, we performed zero moment point (ZMP) analysis for the obtained trajectory model. Whale Optimization Algorithm (WOA) has been gained more interest, due to the fewer number of control parameters, and…
Recent achievements in sensor localization algorithms
Internet Of Things (IOT) is an inevitable result of the evolution of communication and manufacturing of small low power and effective micro electro mechanical systems (MEMS). Wireless Sensor Network (WSN) is Self organized collected sensors that communicate to each other randomly through waves. Node localization is a major challenge for most of WSN applications due…
A new ABC variant for solving inverse kinematics problem in 5 DOF robot arm
While the outcomes of artificial bee colony (ABC) have been encouraging enough, ABC algorithm lacks good compromise between exploration and exploitation. The main aim of this paper is to propose ABC-based algorithm; namely knowledge-based artificial bee colony (K-ABC), that is able to converge quickly and explore the most promising area of the intended search space….
A comparative study of soft computing methods to solve inverse kinematics problem
Robot arms are essential tools nowadays in industries due to its accuracy through high speed manufacturing. One of the most challenging problems in industrial robots is solving inverse kinematics. Inverse Kinematic Problem concerns with finding the values of angles which are related to the desired Cartesian location. With the development of Softcomputing-based methods, it's become…
Modelling and practical studying of heat recovery steam generator (HRSG) drum dynamics and approach point effect on control valves
In this paper, we present a simple procedure to build a model of a heat recovery steam generator (HRSG) evaporator and drum within the environment of MATLAB/Simulink. The HRSG is part of combined cycle power plant that is located at Talkha power station (130 km north of Cairo, capital of Egypt). The model captures the response…
Modeling and control of an interconnected combined cycle gas turbine using fuzzy and ANFIS controllers
This paper presents the dynamic modeling of an interconnected two equal area of conventional combined cycle gas turbine. In addition, fuzzy logic controllers have been designed and applied to improve speed/load control, temperature control, and air flow control. The coordination between fuzzy- controlled speed signal and fuzzy-controlled temperature control signal has been considered in the…
Dynamic power management techniques in multi-core architectures: A survey study
Multi-core processors support all modern electronic devices nowadays. However, power management is one of the most critical issues in the design of today’s microprocessors. The goal of power management is to maximize performance within a given power budget. Power management techniques must balance between the demanding needs for higher performance/throughput and the impact of aggressive…
Enhancing smart grid transient performance using storage device‐based MPC controller
Renewable energy sources (wind turbine and photovoltaic system) are connected to the smart grid to promote the grid power, but the output of these sources is changed due to the sunlight and wind speed variations. Power storage system has the ability to reduce variations in a power system. Battery energy storage system (BESS) and superconducting…
Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO)
This paper presents a Fragmented protein sequence alignment using two-layer PSO (FTLPSO) method to overcome the drawbacks of particle swarm optimization (PSO) and improve its performance in solving multiple sequence alignment (MSA) problem. The standard PSO suffers from the trapping in local optima, and its disability to do better alignment for longer sequences. To overcome…
A modified listless strip based SPIHT for wireless multimedia sensor networks
Set Partitioning In Hierarchical Tree (SPIHT) is considered one of the most important algorithms for reducing the size of the vision data collected by the sensor node within wireless multimedia sensor network (WMSN). The traditional SPIHT algorithm suffers from image coders complexity due to large memory requirement. This is an essential problem for the implementation…
Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengers
This paper presents a new algorithm designed to find the optimal parameters of PID controller. The proposed algorithm is based on hybridizing between differential evolution (DE) and Particle Swarm Optimization with an aging leader and challengers (ALC-PSO) algorithms. The proposed algorithm (ALC-PSODE) is tested on twelve benchmark functions to confirm its performance. It is found…
Design of optimal PID controller using NSGA-II algorithm and level diagram
In multi-objective optimization (MO), there is more than one objective to be optimized. Usually these objectives contradicts each other (ie optimize of one objective cannot be achieved without degradation of other objective). Hence there is no longer a single solution (as in a mono-objective optimization) but a group of trade–off solutions called Pareto points. The…
Image compression algorithms in wireless multimedia sensor networks: A survey
Unlike classical wired networks and wireless sensor networks, WMSN differs from their predecessor’s scalar network basically in the following points; nature and size of data being transmitted, important memory resources, as well as, power consumed per each node for processing and transmission. The most effective solution to overcome those problems is image compression. As the…
Fuzzy-based modeling and control of combined cycle gas turbine plants
The main objective of this paper is to present the dynamic response of combined cycle gas turbine (CCGT) during frequency drop disturbance. The proposed technique is a fuzzy logic control system that used to improve speed, temperature and air flow control signals and make coordination between fuzzy controlled speed signal and fuzzy controlled temperature signal…
A multiobjective dynamic particle swarm optimizer for environmental/economic dispatch problem
This paper proposes a multi-objective Dynamic Random Neighborhood PSO (DRN-PSO) dynamic search based optimization algorithm for solving dual security constrained economic load dispatch problem in modern power systems. The proposed algorithm uses dynamically adjusted Inertia weight to balance global exploration and local exploitation. Numerical results were conducted on IEEE 30-bus test systems and compared to…
RETRACTED: Modified hybrid algorithm for process optimization
Submission of an article to an Elsevier journal requires authors to declare that their work is original and has not appeared in a publication elsewhere. Re-use of any data should be appropriately cited. Due to the overlap in scientific content and the fact that the authors did not include a reference to the article previously…
A smart robot arm design for industrial application
The proposed paper outlines the design and implementation of smart robotic arm that is equipped with a vision system. Three main parts cooperate to perform the control of the proposed arm. Image processing, inverse kinematics and control are involved in the robot arm design. Forward and inverse kinematic are solved using homogenous transformation matrices and…
Multiobjective optimization algorithm for secure economical/emission dispatch problems
Multiobjective real-coded genetic algorithm for economic/environmental dispatch problem
This paper outlines the optimization problem of nonlinear constrained multi-objective economic/environmental dispatch (EED) problems of thermal generators in power systems and presents novel improved real-coded genetic optimization (MO-RCGA) algorithm for solving EED problems. The considered problem minimizes environmental emission and non-smooth fuel cost simultaneously while fulfilling the system operating constraints. The proposed MORCGA technique evolves…
Volume 22• Issue 2• 2013
This paper outlines the optimization problem of nonlinear constrained multi-objective economic/environmental dispatch (EED) problems of thermal generators in power systems and presents novel improved real-coded genetic optimization (MO-RCGA) algorithm for solving EED problems. The considered problem minimizes environmental emission and non-smooth fuel cost simultaneously while fulfilling the system operating constraints. The proposed MO-RCGA technique evolves…
Modified cultural-based genetic algorithm for process optimization
The main weak points in using AI optimization technique are the possibility of being trapped at local minima, being confined to the population space, difficulty to solve heavily nonlinear problems and to make full use of the historical information beside the lack of prediction about the search space. In this paper, a hybrid optimization technique;…
Cultural-Based Genetic Algorithm: Design and Real World Applications
Due to their excellent performance in solving combinatorial optimization problems, metaheuristics algorithms such as genetic algorithms GA (Sareni and Krahenbuhl, 1998; Karr and Freeman, 1999; and Chambers, 1995), simulated annealing SA (Reznik, 1997 and Gill et al., 1981) and tabu search TS make up another class of search methods that has been adopted to efficiently…
Genetic annealing optimization: Design and real world applications
Both simulated annealing (SA) and the genetic algorithms (GA) are stochastic and derivative-free optimization technique. SA operates on one solution at a time, while the GA maintains a large population of solutions, which are optimized simultaneously. Thus, the genetic algorithm takes advantage of the experience gained in the past exploration of the solution space. Since…
Parameter identification problem: Real-coded GA approach
Parameter identification problem will be presented, and solved through our new real-coded genetic Algorithm. The algorithm is a modified version from normal GA but it includes biased initialization, dynamic parameters, and elitism. The algorithm will be tested on three cases.
Innovative Imaging in Neurological Disorders: Bridging Engineering and Medicine
a) Neuro-AI Integration: By introducing a new AI framework for understanding temporal structure in silent image sequences in CATS (Context-Aware Temporal synthesis) that working without motion cues or audio by using curvature-aware alignment, symmetry-enforced attention and sematic memory. CATS shows achievement up to 15% relative improvement in egocentric video understanding, stable regime separation and accuracy…
Genetic algorithm for the design and optimization of a shell and tube heat exchanger from a performance point of view
A new approach to optimize the design of a shell and tube heat exchanger (STHX) is developed via a genetic algorithm (GA) to get the optimal configuration from a performance point of view. The objective is to develop and test a model for optimizing the early design stage of the STHX and solve the design…
Intelligent Parcel Delivery Scheduling Using Truck-Drones to Cut Down Time and Cost
In the evolving landscape of logistics, drone technology presents a solution to the challenges posed by traditional ground-based deliveries, such as traffic congestion and unforeseen road closures. This research addresses the Truck-Drone Delivery Problem (TDDP), wherein a truck collaborates with a drone, acting as a mobile charging and storage unit. Although the Traveling Salesman Problem…
Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge Control
The incidence of surge within axial compressors profoundly influences the efficacy and reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on the precise and efficient forecasting of critical characteristics during such occurrences. This paper presents a novel approach for predicting the surge phenomenon in axial compressors. Surge is a major issue in…
2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)| 978-1-6654-7275-3/23/$31.00© 2023 IEEE| DOI: 10.1109/ICAISC56366. 2023.10085431
Table of Contents Page 1 ICAISA 2023 Table of Contents title authors page Towards Green and Computing Approaches to Establish Intelligent Transportation Systems (ITS) in KSA Saleh Ateeq Almutairi 1 Intelligent Trash Bin for Smart Cities Wesam Rohouma, Ahmed Mohamed, Yara Elsayed, Hassan Mahasneh and Awni Al Otoom 7 Eco-Friendly IoT Solutions for Smart Cities…