Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes...Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).展开更多
Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,a...Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,and stage of diagnosis,which directly impacts clinical decision-making.Various biological entities,including genes,proteins,mRNAs,miRNAs,and metabolites,contribute to cancer development.The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers.This integrative approach supports the notion that cancer is fundamentally driven by such alterations,enabling the discovery ofmolecular signatures for precision oncology.This reviewexplores the role of AI-drivenmulti-omics analyses in cancer medicine,emphasizing their potential to identify novel biomarkers and therapeutic targets,enhance understanding of Tumor biology,and address integration challenges in clinical workflows.Network biology analyzes identified ERBB2,KRAS,and TP53 as top hub genes in lung cancer based on Maximal Clique Centrality(MCC)scores.In contrast,TP53,ERBB2,ESR1,MYC,and BRCA1 emerged as central regulators in breast cancer,linked to cell proliferation,hormonal signaling,and genomic stability.The review also discusses how specific Artificial Intelligence(AI)algorithms can streamline the integration of heterogeneous datasets,facilitate the interpretation of the tumor microenvironment,and support data-driven clinical strategies.展开更多
The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyeffic...The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.展开更多
The development of human-robot interaction has been continu-ously increasing for the last decades.Through this development,it has become simpler and safe interactions using a remotely controlled telepresence robot in ...The development of human-robot interaction has been continu-ously increasing for the last decades.Through this development,it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment.The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5G and 6G.However,the design of the phys-ical parameters,e.g.,maneuverability,controllability,and stability,still needs attention.Therefore,the paper aims to present a systematic,controlled design and implementation of a telepresence mobile robot.The primary focus of this paper is to perform the computational analysis and experimental implementa-tion design with sophisticated position control,which autonomously controls the robot’s position and speed when reaching an obstacle.A system model and a position controller design are developed with root locus points.The design robot results are verified experimentally,showing the robot’s agreement and control in the desired position.The robot was tested by considering various parameters:driving straight ahead,right turn,self-localization and complex path.The results prove that the proposed approach is flexible and adaptable and gives a better alternative.The experimental results show that the proposed method significantly minimizes the obstacle hits.展开更多
The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integra...The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.展开更多
The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obsta...The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obstacle avoidance.Selecting the correct components is a critical part of the design process.However,this can be a particularly difficult task,especially for novices as there are several technologies and components available on the market,each with their own individual advantages and disadvantages.For example,satellite-based navigation components should be avoided when designing indoor UAVs.Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption.Another issue is the number of vendors on the market,each trying to sell their hardware solutions which often incorporate similar technologies.The aim of this paper is to serve as a guide,proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts.The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements.The selection methods are based on easy-to-follow flow charts.A comparison of the various hardware components specifications is also included as part of this work.展开更多
Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study h...Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined period.Towards this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by COVID-19.The analysis was done city-wise and also included the number of recoveries recorded in different cities.Furthermore,the study also discusses the impact of COVID-19 on the economy.For conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of COVID-19.As an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective Computer Science technique that can be used by healthcare professionals.For this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(F.AHP.TOPSIS).After prioritizing the various Computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>A3.Since the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.展开更多
Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were ...Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus.Such enforced alienation affected both the mental and social condition of people significantly.Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement.However,COVID-19 brought all such communication to a grinding halt.Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets.The pandemic has shoved the entire planet into an unstable state.The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia.To achieve this objective,the study analyzes two perspectives:the early approach,and the late approach of COVID-19 and the consequent effects on different aspects of the society.We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society.Findings of this research study indicate that financial resources were the worst affected.Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people.Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.展开更多
The basic unit in life is cell.?It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these p...The basic unit in life is cell.?It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing?the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method,?we developed a new predictor called “pLoc-mGpos” by in-depth extracting the key information from GO (Gene Ontology) into the Chou’s general PseAAC (Pseudo Amino Acid Composition)?for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to “iLoc-Gpos”, the state-of-the-art predictor for the same purpose.?To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.展开更多
The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of th...The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of the recent tech- niques is Augmented Reality (AR). The AR is a new technology which is capable of presenting possibilities that are difficult for other technologies to offer and meet. Nowadays, numerous augmented reality applications have been used in the industry of different kinds and disseminated all over the world. AR will really alter the way individuals view the world. The AR is yet in its initial phases of research and development at different colleges and high-tech institutes. Throughout the last years, AR apps became transportable and generally available on various devices. Besides, AR be- gins to occupy its place in our audio-visual media and to be used in various fields in our life in tangible and exciting ways such as news, sports and is used in many domains in our life such as electronic commerce, promotion, design, and business. In addition, AR is used to facilitate the learning whereas it enables students to access location-specific infor- mation provided through various sources. Such growth and spread of AR applications pushes organizations to compete one another, every one of them exerts its best to gain the customers. This paper provides a comprehensive study of AR including its history, architecture, applications, current challenges and future trends.展开更多
The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision...The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision approaches.In multiple real-life applications,for example,social media,content-based face picture retrieval is a well-invested technique for large-scale databases,where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures.Humans widely employ faces for recognizing and identifying people.Thus,face recognition through formal or personal pictures is increasingly used in various real-life applications,such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals.However,such face image retrieval becomes more challenging in large-scale databases,where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images.This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing(LSH)for facial hard and soft biometrics as(Hard BioHash)and(Soft BioHash),respectively,to be used as a search input for retrieving the top-k matching faces.Moreover,we propose the multi-biometric score-level fusion of both face hard and soft BioHashes(Hard-Soft BioHash Fusion)for further augmented face image retrieval.The experimental outcomes applied on the Labeled Faces in the Wild(LFW)dataset and the related attributes dataset(LFW-attributes),demonstrate that the retrieval performance of the suggested fusion approach(Hard-Soft BioHash Fusion)significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation,where the suggested method provides an augmented accuracy of 87%when executed on 1000 specimens and 77%on 5743 samples.These results remarkably outperform the results of the Hard BioHash method by(50%on the 1000 samples and 30%on the 5743 samples),and the Soft BioHash method by(78%on the 1000 samples and 63%on the 5743 samples).展开更多
Diabetes is a chronic health condition that impairs the body’s ability to convert food to energy,recognized by persistently high levels of blood glucose.Undiagnosed diabetes can cause many complications,including ret...Diabetes is a chronic health condition that impairs the body’s ability to convert food to energy,recognized by persistently high levels of blood glucose.Undiagnosed diabetes can cause many complications,including retinopathy,nephropathy,neuropathy,and other vascular disorders.Machine learning methods can be very useful for disease identification,prediction,and treatment.This paper proposes a new ensemble learning approach for type 2 diabetes prediction based on a hybrid meta-classifier of fuzzy clustering and logistic regression.The proposed approach consists of two levels.First,a baselearner comprising six machine learning algorithms is utilized for predicting diabetes.Second,a hybrid meta-learner that combines fuzzy clustering and logistic regression is employed to appropriately integrate predictions from the base-learners and provide an accurate prediction of diabetes.The hybrid metalearner employs the Fuzzy C-means Clustering(FCM)algorithm to generate highly significant clusters of predictions from base-learners.The predictions of base-learners and their fuzzy clusters are then employed as inputs to the Logistic Regression(LR)algorithm,which generates the final diabetes prediction result.Experiments were conducted using two publicly available datasets,the Pima Indians Diabetes Database(PIDD)and the Schorling Diabetes Dataset(SDD)to demonstrate the efficacy of the proposed method for predicting diabetes.When compared with other models,the proposed approach outperformed them and obtained the highest prediction accuracies of 99.00%and 95.20%using the PIDD and SDD datasets,respectively.展开更多
With the rapid miniaturization in sensor technology,Internet-ofDrones(IoD)has delighted researchers towards information transmission security among drones with the control station server(CSS).In IoD,the drone is diffe...With the rapid miniaturization in sensor technology,Internet-ofDrones(IoD)has delighted researchers towards information transmission security among drones with the control station server(CSS).In IoD,the drone is different in shapes,sizes,characteristics,and configurations.It can be classified on the purpose of its deployment,either in the civilian or military domain.Drone’s manufacturing,equipment installation,power supply,multi-rotor system,and embedded sensors are not issues for researchers.The main thing is to utilize a drone for a complex and sensitive task using an infrastructureless/self-organization/resource-less network type called Flying Ad Hoc Network(FANET).Monitoring data transmission traffic,emergency and rescue operations,border surveillance,search and physical phenomenon sensing,and so on can be achieved by developing a robust mutual authentication and cross-verification scheme for IoD deployment civilian drones.Although several protocols are available in the literature,they are either design issues or suffering from other vulnerabilities;still,no one claims with conviction about foolproof security mechanisms.Therefore,in this paper,the researchers highlighted the major deficits in prior protocols of the domain,i.e.,these protocols are either vulnerable to forgery,side channel,stolen-verifier attacks,or raised the outdated data transmission flaw.In order to overcome these loopholes and provide a solution to the existing vulnerabilities,this paper proposed an improved and robust public key infrastructure(PKI)based authentication scheme for the IoD environment.The proposed protocol’s security analysis section has been conducted formally using BAN(Burrows-Abadi-Needham)logic,ProVerif2.03 simulation,and informally using discussion/pragmatic illustration.While the performance analysis section of the paper has been assessed by considering storage,computation,and communication cost.Upon comparing the proposed protocol with prior works,it has been demonstrated that it is efficient and effective and recommended for practical implementation in the IoD environment.展开更多
The main idea behind the present research is to design a state-feedback controller for an underactuated nonlinear rotary inverted pendulum module by employing the linear quadratic regulator(LQR)technique using local a...The main idea behind the present research is to design a state-feedback controller for an underactuated nonlinear rotary inverted pendulum module by employing the linear quadratic regulator(LQR)technique using local approximation.The LQR is an excellent method for developing a controller for nonlinear systems.It provides optimal feedback to make the closed-loop system robust and stable,rejecting external disturbances.Model-based optimal controller for a nonlinear system such as a rotatory inverted pendulum has not been designed and implemented using Newton-Euler,Lagrange method,and local approximation.Therefore,implementing LQR to an underactuated nonlinear system was vital to design a stable controller.A mathematical model has been developed for the controller design by utilizing the Newton-Euler,Lagrange method.The nonlinear model has been linearized around an equilibrium point.Linear and nonlinear models have been compared to find the range in which linear and nonlinear models’behaviour is similar.MATLAB LQR function and system dynamics have been used to estimate the controller parameters.For the performance evaluation of the designed controller,Simulink has been used.Linear and nonlinear models have been simulated along with the designed controller.Simulations have been performed for the designed controller over the linear and nonlinear system under different conditions through varying system variables.The results show that the system is stable and robust enough to act against external disturbances.The controller maintains the rotary inverted pendulum in an upright position and rejects disruptions like falling under gravitational force or any external disturbance by adjusting the rotation of the horizontal link in both linear and nonlinear environments in a specific range.The controller has been practically designed and implemented.It is vivid from the results that the controller is robust enough to reject the disturbances in milliseconds and keeps the pendulum arm deflection angle to zero degrees.展开更多
Smart environments offer various services,including smart cities,ehealthcare,transportation,and wearable devices,generating multiple traffic flows with different Quality of Service(QoS)demands.Achieving the desired Qo...Smart environments offer various services,including smart cities,ehealthcare,transportation,and wearable devices,generating multiple traffic flows with different Quality of Service(QoS)demands.Achieving the desired QoS with security in this heterogeneous environment can be challenging due to traffic flows and device management,unoptimized routing with resource awareness,and security threats.Software Defined Networks(SDN)can help manage these devices through centralized SDN controllers and address these challenges.Various schemes have been proposed to integrate SDN with emerging technologies for better resource utilization and security.Software Defined Wireless Body Area Networks(SDWBAN)and Software Defined Internet of Things(SDIoT)are the recently introduced frameworks to overcome these challenges.This study surveys the existing SDWBAN and SDIoT routing and security challenges.The paper discusses each solution in detail and analyses its weaknesses.It covers SDWBAN frameworks for efficient management of WBAN networks,management of IoT devices,and proposed security mechanisms for IoT and data security in WBAN.The survey provides insights into the state-of-the-art in SDWBAN and SDIoT routing with resource awareness and security threats.Finally,this study highlights potential areas for future research.展开更多
Distributed denial of service (DDoS) attacks continues to grow as a threat to organizations worldwide. From the first known attack in 1999 to the highly publicized Operation Ababil, the DDoS attacks have a history of ...Distributed denial of service (DDoS) attacks continues to grow as a threat to organizations worldwide. From the first known attack in 1999 to the highly publicized Operation Ababil, the DDoS attacks have a history of flooding the victim network with an enormous number of packets, hence exhausting the resources and preventing the legitimate users to access them. After having standard DDoS defense mechanism, still attackers are able to launch an attack. These inadequate defense mechanisms need to be improved and integrated with other solutions. The purpose of this paper is to study the characteristics of DDoS attacks, various models involved in attacks and to provide a timeline of defense mechanism with their improvements to combat DDoS attacks. In addition to this, a novel scheme is proposed to detect DDoS attack efficiently by using MapReduce programming model.展开更多
A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed docume...A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.展开更多
Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties repo...Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.展开更多
Routing protocols in Mobile Ad Hoc Networks(MANETs)operate with Expanding Ring Search(ERS)mechanism to avoid ooding in the network while tracing step.ERS mechanism searches the network with discerning Time to Live(TTL...Routing protocols in Mobile Ad Hoc Networks(MANETs)operate with Expanding Ring Search(ERS)mechanism to avoid ooding in the network while tracing step.ERS mechanism searches the network with discerning Time to Live(TTL)values described by respective routing protocol that save both energy and time.This work exploits the relation between the TTL value of a packet,trafc on a node and ERS mechanism for routing in MANETs and achieves an Adaptive ERS based Per Hop Behavior(AERSPHB)rendition of requests handling.Each search request is classied based on ERS attributes and then processed for routing while monitoring the node trafc.Two algorithms are designed and examined for performance under exhaustive parametric setup and employed on adaptive premises to enhance the performance of the network.The network is tested under congestion scenario that is based on buffer utilization at node level and link utilization via back-off stage of Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA).Both the link and node level congestion is handled through retransmission and rerouting the packets based on ERS parameters.The aim is to drop the packets that are exhausting the network energy whereas forward the packets nearer to the destination with priority.Extensive simulations are carried out for network scalability,node speed and network terrain size.Our results show that the proposed models attain evident performance enhancement.展开更多
Today,due to the pandemic of COVID-19 the entire world is facing a serious health crisis.According to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmissio...Today,due to the pandemic of COVID-19 the entire world is facing a serious health crisis.According to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of COVID-19.The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places.Therefore,it is very difficult to manually monitor people in overcrowded areas.This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19.This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked.The proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces.Moreover,this work applies to real-time and can be implemented in any public service area.展开更多
基金funded by the Northern Border University,Arar,KSA,under the project number“NBU-FFR-2025-3555-07”.
文摘Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).
基金funded by KAU Endowment(WAQF)at King Abdulaziz University,Jeddah,Saudi Arabia.
文摘Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,and stage of diagnosis,which directly impacts clinical decision-making.Various biological entities,including genes,proteins,mRNAs,miRNAs,and metabolites,contribute to cancer development.The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers.This integrative approach supports the notion that cancer is fundamentally driven by such alterations,enabling the discovery ofmolecular signatures for precision oncology.This reviewexplores the role of AI-drivenmulti-omics analyses in cancer medicine,emphasizing their potential to identify novel biomarkers and therapeutic targets,enhance understanding of Tumor biology,and address integration challenges in clinical workflows.Network biology analyzes identified ERBB2,KRAS,and TP53 as top hub genes in lung cancer based on Maximal Clique Centrality(MCC)scores.In contrast,TP53,ERBB2,ESR1,MYC,and BRCA1 emerged as central regulators in breast cancer,linked to cell proliferation,hormonal signaling,and genomic stability.The review also discusses how specific Artificial Intelligence(AI)algorithms can streamline the integration of heterogeneous datasets,facilitate the interpretation of the tumor microenvironment,and support data-driven clinical strategies.
基金funded by the deanship of scientific research(DSR),King Abdukaziz University,Jeddah,under grant No.(G-1436-611-225)。
文摘The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.
基金supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University under the research project (PSAU/2023/01/23001).
文摘The development of human-robot interaction has been continu-ously increasing for the last decades.Through this development,it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment.The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5G and 6G.However,the design of the phys-ical parameters,e.g.,maneuverability,controllability,and stability,still needs attention.Therefore,the paper aims to present a systematic,controlled design and implementation of a telepresence mobile robot.The primary focus of this paper is to perform the computational analysis and experimental implementa-tion design with sophisticated position control,which autonomously controls the robot’s position and speed when reaching an obstacle.A system model and a position controller design are developed with root locus points.The design robot results are verified experimentally,showing the robot’s agreement and control in the desired position.The robot was tested by considering various parameters:driving straight ahead,right turn,self-localization and complex path.The results prove that the proposed approach is flexible and adaptable and gives a better alternative.The experimental results show that the proposed method significantly minimizes the obstacle hits.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.(GPIP:1074-612-2024).
文摘The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.
文摘The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obstacle avoidance.Selecting the correct components is a critical part of the design process.However,this can be a particularly difficult task,especially for novices as there are several technologies and components available on the market,each with their own individual advantages and disadvantages.For example,satellite-based navigation components should be avoided when designing indoor UAVs.Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption.Another issue is the number of vendors on the market,each trying to sell their hardware solutions which often incorporate similar technologies.The aim of this paper is to serve as a guide,proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts.The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements.The selection methods are based on easy-to-follow flow charts.A comparison of the various hardware components specifications is also included as part of this work.
文摘Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined period.Towards this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by COVID-19.The analysis was done city-wise and also included the number of recoveries recorded in different cities.Furthermore,the study also discusses the impact of COVID-19 on the economy.For conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of COVID-19.As an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective Computer Science technique that can be used by healthcare professionals.For this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(F.AHP.TOPSIS).After prioritizing the various Computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>A3.Since the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.
基金Funding for this study was received from the Ministry of Education andDeanship of Scientific Research at King Abdulaziz University, Kingdom of Saudi Arabia underthe Grant No. IFPHI-267-611-2020.
文摘Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus.Such enforced alienation affected both the mental and social condition of people significantly.Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement.However,COVID-19 brought all such communication to a grinding halt.Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets.The pandemic has shoved the entire planet into an unstable state.The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia.To achieve this objective,the study analyzes two perspectives:the early approach,and the late approach of COVID-19 and the consequent effects on different aspects of the society.We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society.Findings of this research study indicate that financial resources were the worst affected.Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people.Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.
文摘The basic unit in life is cell.?It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing?the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method,?we developed a new predictor called “pLoc-mGpos” by in-depth extracting the key information from GO (Gene Ontology) into the Chou’s general PseAAC (Pseudo Amino Acid Composition)?for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to “iLoc-Gpos”, the state-of-the-art predictor for the same purpose.?To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.
文摘The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of the recent tech- niques is Augmented Reality (AR). The AR is a new technology which is capable of presenting possibilities that are difficult for other technologies to offer and meet. Nowadays, numerous augmented reality applications have been used in the industry of different kinds and disseminated all over the world. AR will really alter the way individuals view the world. The AR is yet in its initial phases of research and development at different colleges and high-tech institutes. Throughout the last years, AR apps became transportable and generally available on various devices. Besides, AR be- gins to occupy its place in our audio-visual media and to be used in various fields in our life in tangible and exciting ways such as news, sports and is used in many domains in our life such as electronic commerce, promotion, design, and business. In addition, AR is used to facilitate the learning whereas it enables students to access location-specific infor- mation provided through various sources. Such growth and spread of AR applications pushes organizations to compete one another, every one of them exerts its best to gain the customers. This paper provides a comprehensive study of AR including its history, architecture, applications, current challenges and future trends.
基金supported and funded by KAU Scientific Endowment,King Abdulaziz University,Jeddah,Saudi Arabia,grant number 077416-04.
文摘The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision approaches.In multiple real-life applications,for example,social media,content-based face picture retrieval is a well-invested technique for large-scale databases,where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures.Humans widely employ faces for recognizing and identifying people.Thus,face recognition through formal or personal pictures is increasingly used in various real-life applications,such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals.However,such face image retrieval becomes more challenging in large-scale databases,where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images.This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing(LSH)for facial hard and soft biometrics as(Hard BioHash)and(Soft BioHash),respectively,to be used as a search input for retrieving the top-k matching faces.Moreover,we propose the multi-biometric score-level fusion of both face hard and soft BioHashes(Hard-Soft BioHash Fusion)for further augmented face image retrieval.The experimental outcomes applied on the Labeled Faces in the Wild(LFW)dataset and the related attributes dataset(LFW-attributes),demonstrate that the retrieval performance of the suggested fusion approach(Hard-Soft BioHash Fusion)significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation,where the suggested method provides an augmented accuracy of 87%when executed on 1000 specimens and 77%on 5743 samples.These results remarkably outperform the results of the Hard BioHash method by(50%on the 1000 samples and 30%on the 5743 samples),and the Soft BioHash method by(78%on the 1000 samples and 63%on the 5743 samples).
基金The authors extend their appreciation to the Deputyship for Research&Inno-vation,Ministry of Education in Saudi Arabia“for funding this research work through the project number IFPHI-193-830-2020”and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Diabetes is a chronic health condition that impairs the body’s ability to convert food to energy,recognized by persistently high levels of blood glucose.Undiagnosed diabetes can cause many complications,including retinopathy,nephropathy,neuropathy,and other vascular disorders.Machine learning methods can be very useful for disease identification,prediction,and treatment.This paper proposes a new ensemble learning approach for type 2 diabetes prediction based on a hybrid meta-classifier of fuzzy clustering and logistic regression.The proposed approach consists of two levels.First,a baselearner comprising six machine learning algorithms is utilized for predicting diabetes.Second,a hybrid meta-learner that combines fuzzy clustering and logistic regression is employed to appropriately integrate predictions from the base-learners and provide an accurate prediction of diabetes.The hybrid metalearner employs the Fuzzy C-means Clustering(FCM)algorithm to generate highly significant clusters of predictions from base-learners.The predictions of base-learners and their fuzzy clusters are then employed as inputs to the Logistic Regression(LR)algorithm,which generates the final diabetes prediction result.Experiments were conducted using two publicly available datasets,the Pima Indians Diabetes Database(PIDD)and the Schorling Diabetes Dataset(SDD)to demonstrate the efficacy of the proposed method for predicting diabetes.When compared with other models,the proposed approach outperformed them and obtained the highest prediction accuracies of 99.00%and 95.20%using the PIDD and SDD datasets,respectively.
文摘With the rapid miniaturization in sensor technology,Internet-ofDrones(IoD)has delighted researchers towards information transmission security among drones with the control station server(CSS).In IoD,the drone is different in shapes,sizes,characteristics,and configurations.It can be classified on the purpose of its deployment,either in the civilian or military domain.Drone’s manufacturing,equipment installation,power supply,multi-rotor system,and embedded sensors are not issues for researchers.The main thing is to utilize a drone for a complex and sensitive task using an infrastructureless/self-organization/resource-less network type called Flying Ad Hoc Network(FANET).Monitoring data transmission traffic,emergency and rescue operations,border surveillance,search and physical phenomenon sensing,and so on can be achieved by developing a robust mutual authentication and cross-verification scheme for IoD deployment civilian drones.Although several protocols are available in the literature,they are either design issues or suffering from other vulnerabilities;still,no one claims with conviction about foolproof security mechanisms.Therefore,in this paper,the researchers highlighted the major deficits in prior protocols of the domain,i.e.,these protocols are either vulnerable to forgery,side channel,stolen-verifier attacks,or raised the outdated data transmission flaw.In order to overcome these loopholes and provide a solution to the existing vulnerabilities,this paper proposed an improved and robust public key infrastructure(PKI)based authentication scheme for the IoD environment.The proposed protocol’s security analysis section has been conducted formally using BAN(Burrows-Abadi-Needham)logic,ProVerif2.03 simulation,and informally using discussion/pragmatic illustration.While the performance analysis section of the paper has been assessed by considering storage,computation,and communication cost.Upon comparing the proposed protocol with prior works,it has been demonstrated that it is efficient and effective and recommended for practical implementation in the IoD environment.
文摘The main idea behind the present research is to design a state-feedback controller for an underactuated nonlinear rotary inverted pendulum module by employing the linear quadratic regulator(LQR)technique using local approximation.The LQR is an excellent method for developing a controller for nonlinear systems.It provides optimal feedback to make the closed-loop system robust and stable,rejecting external disturbances.Model-based optimal controller for a nonlinear system such as a rotatory inverted pendulum has not been designed and implemented using Newton-Euler,Lagrange method,and local approximation.Therefore,implementing LQR to an underactuated nonlinear system was vital to design a stable controller.A mathematical model has been developed for the controller design by utilizing the Newton-Euler,Lagrange method.The nonlinear model has been linearized around an equilibrium point.Linear and nonlinear models have been compared to find the range in which linear and nonlinear models’behaviour is similar.MATLAB LQR function and system dynamics have been used to estimate the controller parameters.For the performance evaluation of the designed controller,Simulink has been used.Linear and nonlinear models have been simulated along with the designed controller.Simulations have been performed for the designed controller over the linear and nonlinear system under different conditions through varying system variables.The results show that the system is stable and robust enough to act against external disturbances.The controller maintains the rotary inverted pendulum in an upright position and rejects disruptions like falling under gravitational force or any external disturbance by adjusting the rotation of the horizontal link in both linear and nonlinear environments in a specific range.The controller has been practically designed and implemented.It is vivid from the results that the controller is robust enough to reject the disturbances in milliseconds and keeps the pendulum arm deflection angle to zero degrees.
基金supporting this research through the Post-Doctoral Fellowship Scheme under Grant Q.J130000.21A2.06E03 and Q.J130000.2409.08G77.
文摘Smart environments offer various services,including smart cities,ehealthcare,transportation,and wearable devices,generating multiple traffic flows with different Quality of Service(QoS)demands.Achieving the desired QoS with security in this heterogeneous environment can be challenging due to traffic flows and device management,unoptimized routing with resource awareness,and security threats.Software Defined Networks(SDN)can help manage these devices through centralized SDN controllers and address these challenges.Various schemes have been proposed to integrate SDN with emerging technologies for better resource utilization and security.Software Defined Wireless Body Area Networks(SDWBAN)and Software Defined Internet of Things(SDIoT)are the recently introduced frameworks to overcome these challenges.This study surveys the existing SDWBAN and SDIoT routing and security challenges.The paper discusses each solution in detail and analyses its weaknesses.It covers SDWBAN frameworks for efficient management of WBAN networks,management of IoT devices,and proposed security mechanisms for IoT and data security in WBAN.The survey provides insights into the state-of-the-art in SDWBAN and SDIoT routing with resource awareness and security threats.Finally,this study highlights potential areas for future research.
文摘Distributed denial of service (DDoS) attacks continues to grow as a threat to organizations worldwide. From the first known attack in 1999 to the highly publicized Operation Ababil, the DDoS attacks have a history of flooding the victim network with an enormous number of packets, hence exhausting the resources and preventing the legitimate users to access them. After having standard DDoS defense mechanism, still attackers are able to launch an attack. These inadequate defense mechanisms need to be improved and integrated with other solutions. The purpose of this paper is to study the characteristics of DDoS attacks, various models involved in attacks and to provide a timeline of defense mechanism with their improvements to combat DDoS attacks. In addition to this, a novel scheme is proposed to detect DDoS attack efficiently by using MapReduce programming model.
基金This research was supported and funded by KAU Scientific Endowment,King Abdulaziz University,Jeddah,Saudi Arabia.
文摘A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.
基金funded by Institutional Fund Projects under Grant No(IFPNC-001-611-2020).
文摘Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.
文摘Routing protocols in Mobile Ad Hoc Networks(MANETs)operate with Expanding Ring Search(ERS)mechanism to avoid ooding in the network while tracing step.ERS mechanism searches the network with discerning Time to Live(TTL)values described by respective routing protocol that save both energy and time.This work exploits the relation between the TTL value of a packet,trafc on a node and ERS mechanism for routing in MANETs and achieves an Adaptive ERS based Per Hop Behavior(AERSPHB)rendition of requests handling.Each search request is classied based on ERS attributes and then processed for routing while monitoring the node trafc.Two algorithms are designed and examined for performance under exhaustive parametric setup and employed on adaptive premises to enhance the performance of the network.The network is tested under congestion scenario that is based on buffer utilization at node level and link utilization via back-off stage of Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA).Both the link and node level congestion is handled through retransmission and rerouting the packets based on ERS parameters.The aim is to drop the packets that are exhausting the network energy whereas forward the packets nearer to the destination with priority.Extensive simulations are carried out for network scalability,node speed and network terrain size.Our results show that the proposed models attain evident performance enhancement.
基金This work was supported King Abdulaziz University under grant number IFPHI-033-611-2020.
文摘Today,due to the pandemic of COVID-19 the entire world is facing a serious health crisis.According to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of COVID-19.The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places.Therefore,it is very difficult to manually monitor people in overcrowded areas.This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19.This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked.The proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces.Moreover,this work applies to real-time and can be implemented in any public service area.
