Cross vision transformer with enhanced Growth Optimizer for breast cancer detection in IoMT environment.

The recent advances in artificial intelligence modern approaches can play vital roles in the Internet of Medical Things (IoMT). Automatic diagnosis is one of the most important topics in the IoMT, including cancer diagnosis. Breast cancer is one of the top causes of death among women. Accurate diagnosis and early detection of breast cancer can improve the survival rate of patients. Deep learning models have demonstrated outstanding potential in accurately detecting and diagnosing breast cancer. This paper proposes a novel technology for breast cancer detection using CrossViT as the deep learning model and an enhanced version of the Growth Optimizer algorithm (MGO) as the feature selection method. CrossVit is a hybrid deep learning model that combines the strengths of both convolutional neural networks (CNNs) and transformers. The MGO is a meta-heuristic algorithm that selects the most relevant features from a large pool of features to enhance the performance of the model. The developed approach was evaluated on three publicly available breast cancer datasets and achieved competitive performance compared to other state-of-the-art methods. The results show that the combination of CrossViT and the MGO can effectively identify the most informative features for breast cancer detection, potentially assisting clinicians in making accurate diagnoses and improving patient outcomes. The MGO algorithm improves accuracy by approximately 1.59% on INbreast, 5.00% on MIAS, and 0.79% on MiniDDSM compared to other methods on each respective dataset. The developed approach can also be utilized to improve the Quality of Service (QoS) in the healthcare system as a deployable IoT-based intelligent solution or a decision-making assistance service, enhancing the efficiency and precision of the diagnosis.

The use of urinary kidney injury molecule-1 and neutrophil gelatinase-associated lipocalin for diagnosis of hepato-renal syndrome in advanced cirrhotic patients.

BACKGROUND: Chronic liver disease is a common and important clinical problem.Hepatorenal syndrome (HRS) is a life threatening complication. Serum creatinine (Cr) remains the only conventional indicator of renal function. However, the interpretation of serum Cr level can be confounded by malnutrition and reduced muscle mass often observed in patients with severe liver disease. Here, we present a cross-sectional study to explore the sensitivity and specificity of other markers as urinary KIM-1 and NGAL for cases of HRS. METHODS: Cross-sectional study was conducted on 88 patients who were admitted to Alexandria main university hospital. Enrolled patients were divided in two groups; group 1: patients with advanced liver cirrhosis (child B and C) who have normal kidney functions while group 2: patients who developed HRS. Stata© version 14.2 software package was used for analysis. RESULTS: Group 1 included 18 males and 26 females compared to 25 males and 19 females in group 2 (p = 0.135). Only the urinary KIM-1 showed a statistically significant difference between both groups in the multivariate logistic regression analysis adjusted for gender, serum bilirubin, serum albumin, INR, serum K, AST and ALT levels. CONCLUSION: In conclusion, our study aligns with prior research, as seen in the consistent findings regarding Urinary NGAL elevation in cirrhotic patients with AKI. Urinary KIM-1, independent of Urinary NGAL, may have a role in precisely distinguishing between advanced liver cirrhosis and HRS and merits further exploration.

Boosting Sinh Cosh Optimizer and arithmetic optimization algorithm for improved prediction of biological activities for indoloquinoline derivatives.

Quantitative Structure Activity Relation (QSAR) models are mathematical techniques used to link structural characteristics with biological activities, thus considered a useful tool in drug discovery, hazard evaluation, and identifying potentially lethal molecules. The QSAR regulations are determined by the Organization for Economic Cooperation and Development (OECD). QSAR models are helpful in discovering new drugs and chemicals to treat severe diseases. In order to improve the QSAR model's predictive power for biological activities of naturally occurring indoloquinoline derivatives against different cancer cell lines, a modified machine learning (ML) technique is presented in this paper. The Arithmetic Optimization Algorithm (AOA) operators are used in the suggested model to enhance the performance of the Sinh Cosh Optimizer (SCHO). Moreover, this improvement functions as a feature selection method that eliminates superfluous descriptors. An actual dataset gathered from previously published research is utilized to evaluate the performance of the suggested model. Moreover, a comparison is made between the outcomes of the suggested model and other established methodologies. In terms of pIC50 values for different indoloquinoline derivatives against human MV4-11 (leukemia), human HCT116 (colon cancer), and human A549 (lung cancer) cell lines, the suggested model achieves root mean square error (RMSE) of 0.6822, 0.6787, 0.4411, and 0.4477, respectively. The biological application of indoloquinoline derivatives as possible anticancer medicines is predicted with a high degree of accuracy by the suggested model, as evidenced by these findings.

AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans.

This study aims to develop an AI-enhanced methodology for the expedited and accurate diagnosis of Multiple Sclerosis (MS), a chronic disease affecting the central nervous system leading to progressive impairment. Traditional diagnostic methods are slow and require substantial expertise, underscoring the need for innovative solutions. Our approach involves two phases: initially, extracting features from brain MRI images using first-order histograms, the gray level co-occurrence matrix, and local binary patterns. A unique feature selection technique combining the Sine Cosine Algorithm with the Sea-horse Optimizer is then employed to identify the most significant features. Utilizing the eHealth lab dataset, which includes images from 38 MS patients (mean age 34.1 ± 10.5 years; 17 males, 21 females) and matched healthy controls, our model achieved a remarkable 97.97% detection accuracy using the k-nearest neighbors classifier. Further validation on a larger dataset containing 262 MS cases (199 females, 63 males; mean age 31.26 ± 10.34 years) and 163 healthy individuals (109 females, 54 males; mean age 32.35 ± 10.30 years) demonstrated a 92.94% accuracy for FLAIR images and 91.25% for T2-weighted images with the Random Forest classifier, outperforming existing MS detection methods. These results highlight the potential of the proposed technique as a clinical decision-making tool for the early identification and management of MS.

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection.

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%) and F-Score (90-100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC'2022 benchmarks functions.

Bone metastasis detection method based on improving golden jackal optimization using whale optimization algorithm.

This paper presents a machine learning-based technique for interpreting bone scintigraphy images, focusing on feature extraction and introducing a new feature selection method called GJOW. GJOW enhances the effectiveness of the golden jackal optimization (GJO) algorithm by integrating operators from the whale optimization algorithm (WOA). The technique's performance is evaluated through extensive experiments using 18 benchmark datasets and 581 bone scan images obtained from a gamma camera, including 362 abnormal and 219 normal cases. The results highlight the superior predictive effectiveness of the GJOW algorithm in bone metastasis detection, achieving an accuracy of 71.79% and specificity of 91.14%. The contributions of this study include the introduction of a new machine learning-based approach for detecting bone metastasis using gamma camera scans, leading to improved accuracy in identifying bone metastases. The findings have practical implications for early detection and intervention, potentially improving patient outcomes.

Efficacy of Topical Azithromycin versus Systemic Doxycycline in Treatment of Meibomian Gland Dysfunction.

Background: Ocular surface disease (OSD) is a multifactorial and highly frequent problem. Inadequate or unstable tear film is the main cause, which leads to visual impairments. One of the primary causes of OSD is meibomian gland dysfunction (MGD), with a prevalence of 3.5 to 70%. The aim of this work was to compare the efficacy of azithromycin topical eye drops versus oral doxycycline in MGD individuals. Methods: This prospective comparative cohort research was carried out on 56 patients of both sexes of any age with symptomatic MGD. Randomly, patients were classified into two equal groups: Group 1 was treated twice daily for 4 weeks with topical azithromycin 1% eye drops, while group 2 received oral doxycycline 100 mg capsules twice daily for 4 weeks. Results: In the 1st follow-up, there was a significant difference between the studied groups in pain and discomfort degree (P value = 0.024) as group 1 showed a higher number of patients with a mild pain degree (P value = 0.013) while group 2 showed a higher number of patients with a severe pain degree (P value = 0.022). There was an insignificant difference between the studied groups in moderate pain degree and lid margin telangiectasia. Conjunctivitis, frothy discharge, and meniscus floaters were significantly higher in group 2 than in group 1 (P value = 0.013, 0.028, and 0.031, respectively). In group 1, the break-up time test was significantly higher than in group 2 (P value = 0.023). In the 2nd follow up, in group 2 only meniscus floaters were significantly higher than in group 1 (P value = 0.044), while in group 1 break-up time test was significantly higher than in group 2 (P value = 0.029). Otherwise, there is no significant difference between both the groups. Conclusions: Meibomian gland dysfunction (MGD) could be treated effectively with oral doxycycline and topical azithromycin by improving symptoms, clinical signs, and stabilization of tear film. Moreover, the topical azithromycin group seemed to be superior over the oral doxycycline group in improving the quality of tear film in the short term, having fewer side effects, more compliance, and better tolerability.

An Efficient Artificial Rabbits Optimization Based on Mutation Strategy For Skin Cancer Prediction.

Accurate skin lesion diagnosis is critical for the early detection of melanoma. However, the existing approaches are unable to attain substantial levels of accuracy. Recently, pre-trained Deep Learning (DL) models have been applied to tackle and improve efficiency on tasks such as skin cancer detection instead of training models from scratch. Therefore, we develop a robust model for skin cancer detection with a DL-based model as a feature extraction backbone, which is achieved using MobileNetV3 architecture. In addition, a novel algorithm called the Improved Artificial Rabbits Optimizer (IARO) is introduced, which uses the Gaussian mutation and crossover operator to ignore the unimportant features from those features extracted using MobileNetV3. The PH2, ISIC-2016, and HAM10000 datasets are used to validate the developed approach's efficiency. The empirical results show that the developed approach yields outstanding accuracy results of 87.17% on the ISIC-2016 dataset, 96.79% on the PH2 dataset, and 88.71 % on the HAM10000 dataset. Experiments show that the IARO can significantly improve the prediction of skin cancer.

A Thorough Examination of the Solution Conditions and the Use of Carbon Nanoparticles Made from Commercial Mesquite Charcoal as a Successful Sorbent for Water Remediation.

Water pollution has invaded seas, rivers, and tap water worldwide. This work employed commercial Mesquite charcoal as a low-cost precursor for fabricating Mesquite carbon nanoparticles (MUCNPs) using a ball-milling process. The scanning electron energy-dispersive microscopy results for MUCNPs revealed a particle size range of 52.4-75.0 nm. The particles were composed mainly of carbon with trace amounts of aluminum, potassium, calcium, titanium, and zinc. The X-ray diffraction peaks at 26.76 and 43.28 2θ° ascribed to the (002) and (100) planes indicated a crystalized graphite phase. Furthermore, the lack of FT-IR vibrations above 3000 cm-1 showed that the MUCNPs were not functionalized. The MUCNPs' pore diameter, volume, and surface area were 114.5 Ǻ, 0.363 cm3 g-1, and 113.45 m2 g-1. The batch technique was utilized to investigate MUCNPs' effectiveness in removing chlorohexidine gluconate (CHDNG) from water, which took 90 min to achieve equilibrium and had an adsorption capacity of 65.8 mg g-1. The adsorption of CHDNG followed pseudo-second-order kinetics, with the rate-limiting step being diffusion in the liquid film. The Langmuir isotherm dominated the CHDNG adsorption on the MUCNPs with a correlation coefficient of 0.99. The thermodynamic studies revealed that CHDNG adsorption onto the MUCNPs was exothermic and favorable, and its spontaneity increased inversely with CHDNG concentration. The ball-milling-made MUCNPs demonstrated consistent efficiency through regeneration-reuse cycles.

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks.

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.

Morphology and thickness of corneal endothelial cells in young Sudanese individuals with myopia.

Deviations in corneal endothelium morphology and thickness may indicate corneal abnormalities and could be associated with myopia development. This study aimed to evaluate corneal endothelial cell morphology and central corneal thickness in young individuals with myopia. A prospective study was conducted at Al-Neelain University Eye Hospital between January 2019 and January 2020, including 160 patients with myopia (320 eyes). Data was gathered through clinical assessment of visual acuity, refractive error, and corneal endothelial cells. Results showed that 60% of participants with myopia were female, with a mean age of 21.99±2.8 years and a mean equivalent sphere of -3.19±2.67D. There was a significant difference in endothelial cell degeneration between myopia groups (P<0.001). Corneal guttata occurred in 9.1% of eyes with low myopia and 68.2% with moderate myopia, whereas polymegathism and polymorphism were more prevalent in high myopia. The mean central corneal thickness was 500.50±38.94 µm in low myopia, 497.02±36.23 µm in moderate myopia, and 477.87±43.625 µm in high myopia (P=0.007). The mean endothelial cell number in low myopia was 107.86±21.12, 106.0±24.03 in moderate myopia, and 101.23±18.49 in high myopia (P<0.05). The mean difference in endothelial cell density, coefficient of variation, and hexagonality in low, moderate, and high myopia was not significant (P>0.05). However, Pearson's correlation revealed a significant negative correlation between the degree of myopia and central corneal thickness (r= -0.174, P=0.002) as well as endothelial cell number (r= -0.124, P=0.026). The study concluded that central corneal thickness and endothelial cell number significantly decreased with an increase in the degree of myopia. Corneal guttata was the most common form of endothelial cell degeneration observed in cases of high myopia.

The Pattern of Antimicrobial Prescription at a Tertiary Health Center in Egypt: A Point Survey and Implications.

Background: The misuse of antimicrobials has major consequences, particularly antimicrobial resistance (AMR) and antimicrobials' related adverse effects. So, the WHO proposed antimicrobial surveillance to improve antimicrobials use. This point prevalence survey (PPS) was conducted to illustrate the prevalence of antimicrobial use at Mansoura University hospitals (MUH), Egypt. Methods: The survey process used was adapted from the European survey of antimicrobial resistance with modifications. The survey was conducted from 8 AM to 8 PM daily within 2 weeks. Results: A total of 300 patients received antimicrobials and the prevalence rate of antimicrobial prescription was 79.15%. The major indications of antimicrobials were surgical prophylaxis followed by the treatment of community-acquired infection. The most commonly prescribed initial antimicrobial group was Aminopenicillin ± ß-lactamase inhibitors. Although the purpose for antimicrobial administration was recorded in all cases, the stop/review history was recorded only in 19.6% and local guidelines were not available for 77.6% of antimicrobial prescriptions. The use of combined antimicrobials was common (46.6%), particularly in orthopedic and cardiothoracic surgery. Conclusion: The prevalence of antimicrobial prescription at MUH was high which requires serious actions including reviewing the antimicrobial indication, implementing local prescription guidelines, initiating an antimicrobial stewardship program (ASP), and optimizing infection control measures.

Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting.

The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine-cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.

Efficient and Secure WiFi Signal Booster via Unmanned Aerial Vehicles WiFi Repeater Based on Intelligence Based Localization Swarm and Blockchain.

Recently, the unmanned aerial vehicles (UAV) under the umbrella of the Internet of Things (IoT) in smart cities and emerging communities have become the focus of the academic and industrial science community. On this basis, UAVs have been used in many military and commercial systems as emergency transport and air support during natural disasters and epidemics. In such previous scenarios, boosting wireless signals in remote or isolated areas would need a mobile signal booster placed on UAVs, and, at the same time, the data would be secured by a secure decentralized database. This paper contributes to investigating the possibility of using a wireless repeater placed on a UAV as a mobile booster for weak wireless signals in isolated or rural areas in emergency situations and that the transmitted information is protected from external interference and manipulation. The working mechanism is as follows: one of the UAVs detect a human presence in a predetermined area with the thermal camera and then directs the UAVs to the location to enhance the weak signal and protect the transmitted data. The methodology of localization and clusterization of the UAVs is represented by a swarm intelligence localization (SIL) optimization algorithm. At the same time, the information sent by UAV is protected by blockchain technology as a decentralization database. According to realistic studies and analyses of UAVs localization and clusterization, the proposed idea can improve the amplitude of the wireless signals in far regions. In comparison, this database technique is difficult to attack. The research ultimately supports emergency transport networks, blockchain, and IoT services.

Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things.

Recently, the 6G-enabled Internet of Medical Things (IoMT) has played a key role in the development of functional health systems due to the massive data generated daily from the hospitals. Therefore, the automatic detection and prediction of future risks such as pneumonia and retinal diseases are still under research and study. However, traditional approaches did not yield good results for accurate diagnosis. In this paper, a robust 6G-enabled IoMT framework is proposed for medical image classification with an ensemble learning (EL)-based model. EL is achieved using MobileNet and DenseNet architecture as a feature extraction backbone. In addition, the developed framework uses a modified honey badger algorithm (HBA) based on Levy flight (LFHBA) as a feature selection method that aims to remove the irrelevant features from those extracted features using the EL model. For evaluation of the performance of the proposed framework, the chest X-ray (CXR) dataset and the optical coherence tomography (OCT) dataset were employed. The accuracy of our technique was 87.10% on the CXR dataset and 94.32% on OCT dataset-both very good results. Compared to other current methods, the proposed method is more accurate and efficient than other well-known and popular algorithms.

Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things.

Nowadays, the emerging information technologies in smart handheld devices are motivating the research community to make use of embedded sensors in such devices for healthcare purposes. In particular, inertial measurement sensors such as accelerometers and gyroscopes embedded in smartphones and smartwatches can provide sensory data fusion for human activities and gestures. Thus, the concepts of the Internet of Healthcare Things (IoHT) paradigm can be applied to handle such sensory data and maximize the benefits of collecting and analyzing them. The application areas contain but are not restricted to the rehabilitation of elderly people, fall detection, smoking control, sportive exercises, and monitoring of daily life activities. In this work, a public dataset collected using two smartphones (in pocket and wrist positions) is considered for IoHT applications. Three-dimensional inertia signals of thirteen timestamped human activities such as Walking, Walking Upstairs, Walking Downstairs, Writing, Smoking, and others are registered. Here, an efficient human activity recognition (HAR) model is presented based on efficient handcrafted features and Random Forest as a classifier. Simulation results ensure the superiority of the applied model over others introduced in the literature for the same dataset. Moreover, different approaches to evaluating such models are considered, as well as implementation issues. The accuracy of the current model reaches 98.7% on average. The current model performance is also verified using the WISDM v1 dataset.

Enhanced Feature Selection Based on Integration Containment Neighborhoods Rough Set Approximations and Binary Honey Badger Optimization.

This article appoints a novel model of rough set approximations (RSA), namely, rough set approximation models build on containment neighborhoods RSA (CRSA), that generalize the traditional notions of RSA and obtain valuable consequences by minifying the boundary areas. To justify this extension, it is integrated with the binary version of the honey badger optimization (HBO) algorithm as a feature selection (FS) approach. The main target of using this extension is to assess the quality of selected features. To evaluate the performance of BHBO based on CRSA, a set of ten datasets is used. In addition, the results of BHOB are compared with other well-known FS approaches. The results show the superiority of CRSA over the traditional RS approximations. In addition, they illustrate the high ability of BHBO to improve the classification accuracy overall the compared methods in terms of performance metrics.

An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture.

Reaching a flat network is the main target of future evolved packet core for the 5G mobile networks. The current 4th generation core network is centralized architecture, including Serving Gateway and Packet-data-network Gateway; both act as mobility and IP anchors. However, this architecture suffers from non-optimal routing and intolerable latency due to many control messages. To overcome these challenges, we propose a partially distributed architecture for 5th generation networks, such that the control plane and data plane are fully decoupled. The proposed architecture is based on including a node Multi-session Gateway to merge the mobility and IP anchor gateway functionality. This work presented a control entity with the full implementation of the control plane to achieve an optimal flat network architecture. The impact of the proposed evolved packet Core structure in attachment, data delivery, and mobility procedures is validated through simulation. Several experiments were carried out by using NS-3 simulation to validate the results of the proposed architecture. The Numerical analysis is evaluated in terms of total transmission delay, inter and intra handover delay, queuing delay, and total attachment time. Simulation results show that the proposed architecture performance-enhanced end-to-end latency over the legacy architecture.

Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm.

This study proposes a novel framework to improve intrusion detection system (IDS) performance based on the data collected from the Internet of things (IoT) environments. The developed framework relies on deep learning and metaheuristic (MH) optimization algorithms to perform feature extraction and selection. A simple yet effective convolutional neural network (CNN) is implemented as the core feature extractor of the framework to learn better and more relevant representations of the input data in a lower-dimensional space. A new feature selection mechanism is proposed based on a recently developed MH method, called Reptile Search Algorithm (RSA), which is inspired by the hunting behaviors of the crocodiles. The RSA boosts the IDS system performance by selecting only the most important features (an optimal subset of features) from the extracted features using the CNN model. Several datasets, including KDDCup-99, NSL-KDD, CICIDS-2017, and BoT-IoT, were used to assess the IDS system performance. The proposed framework achieved competitive performance in classification metrics compared to other well-known optimization methods applied for feature selection problems.

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19.

The longest common consecutive subsequences (LCCS) play a vital role in revealing the biological relationships between DNA/RNA sequences especially the newly discovered ones such as COVID-19. FLAT is a Fragmented local aligner technique which is an accelerated version of the local pairwise sequence alignment algorithm based on meta-heuristic algorithms. The performance of FLAT needs to be enhanced since the huge length of biological sequences leads to trapping in local optima. This paper introduces a modified version of FLAT based on improving the performance of the BA algorithm by integration with particle swarm optimization (PSO) algorithm based on a novel infection mechanism. The proposed algorithm, named BPINF, depends on finding the best-explored solution using BA operators which can infect the agents during the exploitation phase using PSO operators to move toward it instead of moving toward the best-exploited solution. Hence, moving the solutions toward the two best solutions increase the diversity of generated solutions and avoids trapping in local optima. The infection can be propagated through the agents where each infected agent can transfer the infection to other non-infected agents which enhances the diversification of generated solutions. FLAT using the proposed technique (BPINF) was validated to detect LCCS between a set of real biological sequences with huge lengths besides COVID-19 and other well-known viruses. The performance of BPINF was compared to the enhanced versions of BA in the literature and the relevant studies of FLAT. It has a preponderance to find the LCCS with the highest percentage (88%) which is better than other state-of-the-art methods.