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A High Destruction-Resistant Resilient Networking Platform for Air-to-Ground Cooperative Communications 认领 引用
1
作者 Dong Ping Ren Jiaxin +3 位作者 Guo Jiannan Zhang Yuzhen Liu Qianwen Amr Tolba 《China Communications》 SCIE EI CSCD 2025年第4期27-41,共15页
With the continuous advancement of communication and unmanned aerial vehicle(UAV)technologies,the collaborative operations of diverse platforms,including UAVs and ground vehicles,have been significantly promoted.Howev... With the continuous advancement of communication and unmanned aerial vehicle(UAV)technologies,the collaborative operations of diverse platforms,including UAVs and ground vehicles,have been significantly promoted.However,battlefield uncertainties,such as equipment failures and enemy attacks,can impact these collaborative operations'stability and communication efficiency.To this end,we design a highly destruction-resistant air-ground cooperative resilient networking platform that aims to enhance the robustness of network communications by integrating ground vehicle information for UAV network deployment.It then incorporates the concept of virtual guiding force,enabling the UAV swarm to adaptively configure its network layout based on ground vehicle information,thereby improving network destruction resistance.Simulation results demonstrate that the UAV swarm involved in the proposed platform exhibits balanced flight energy consumption and excellent performance in network destruction resistance. 展开更多
关键词 air-to-ground coordination network destruction resistance SpringBoot UAV
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A certificateless and KGA-secure searchable encryption scheme with constant trapdoors in smart city 认领 引用
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作者 Hongjun Li Debiao He +2 位作者 P.Vijayakumar Fayez Alqahtani Amr Tolba 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期198-209,共12页
Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience ... Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience to smart city services,a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries.Considering the security and privacy of data,data owners transmit sensitive data in its encrypted form to cloud server,which seriously hinders the improvements of potential utilization and efficient sharing.Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption.However,most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners.In this work,by utilizing certificateless encryption and proxy re-encryption,we design an authenticated searchable encryption scheme with constant trapdoors.The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors,and can resist keyword guessing attacks.In addition,data users can generate and upload trapdoors with lower computation and communication overheads.We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance. 展开更多
关键词 Smart city Data retrieval Privacy protection Certificateless cryptography
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Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles 认领 引用
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作者 Kalpesh Jadav Vishal Sorathiya +5 位作者 Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui 《Computers, Materials & Continua》 SCIE EI 2023年第11期2009-2030,共22页
Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehic... Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes. 展开更多
关键词 Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking
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Zero Trust Networks: Evolution and Application from Concept to Practice 认领 引用
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作者 Yongjun Ren Zhiming Wang +3 位作者 Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang 《Computers, Materials & Continua》 SCIE EI 2025年第2期1593-1613,共21页
In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized technology.Zero Trust not ... In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized technology.Zero Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network environments.This paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key technologies.Additionally,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use cases.The paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions. 展开更多
关键词 Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things
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BAHGRF3:Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation 认领 引用
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作者 Muhammad Abrar Ahmad Khan Muhammad Attique Khan +5 位作者 Ateeq Ur Rehman Ahmed Ibrahim Alzahrani Nasser Alalwan Deepak Gupta Saima Ahmed Rahin Yudong Zhang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第2期387-401,共15页
Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework... Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques. 展开更多
关键词 deep learning feature fusion feature optimization gait classification indoor environment machine learning
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Deep reinforcement learning-based spectrum resource allocation for the web of healthcare things with massive integrating wearable gadgets 认领 引用
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作者 Jie Huang Cheng Yang +4 位作者 Fan Yang Shilong Zhang Amr Tolba Alireza Jolfaei Keping Yu 《Digital Communications and Networks》 SCIE EI CSCD 2025年第3期671-680,共10页
With the development of the future Web of Healthcare Things(WoHT),there will be a trend of densely deploying medical sensors with massive simultaneous online communication requirements.The dense deployment and simulta... With the development of the future Web of Healthcare Things(WoHT),there will be a trend of densely deploying medical sensors with massive simultaneous online communication requirements.The dense deployment and simultaneous online communication of massive medical sensors will inevitably generate overlapping interference.This will be extremely challenging to support data transmission at the medical-grade quality of service level.To handle the challenge,this paper proposes a hypergraph interference coordination-aided resource allocation based on the Deep Reinforcement Learning(DRL)method.Specifically,we build a novel hypergraph interference model for the considered WoHT by analyzing the impact of the overlapping interference.Due to the high complexity of directly solving the hypergraph interference model,the original resource allocation problem is converted into a sequential decision-making problem through the Markov Decision Process(MDP)modeling method.Then,a policy and value-based resource allocation algorithm is proposed to solve this problem under simultaneous online communication and dense deployment.In addition,to enhance the exploration ability of the optimal allocation strategy for the agent,we propose a resource allocation algorithm with an asynchronous parallel architecture.Simulation results verify that the proposed algorithms can achieve higher network throughput than the existing algorithms in the considered WoHT scenario. 展开更多
关键词 Web of healthcare things Hypergraph Interference coordination Deep reinforcement learning
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An Overlapped Multihead Self-Attention-Based Feature Enhancement Approach for Ocular Disease Image Recognition 认领 引用
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作者 Peng Xiao Haiyu Xu +3 位作者 Peng Xu Zhiwei Guo Amr Tolba Osama Alfarraj 《Computers, Materials & Continua》 SCIE EI 2025年第11期2999-3022,共24页
Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features i... Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making. 展开更多
关键词 Overlapping multi-head self-attention deep learning cross-modal dynamic fusion multi-level fusion
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A Secure Authentication Indexed Choice-Based Graphical Password Scheme for Web Applications and ATMs 认领 引用
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作者 Sameh Zarif Hadier Moawad +4 位作者 Khalid Amin Abdullah Alharbi Wail SElkilani Shouze Tang Marian Wagdy 《Computer Systems Science & Engineering》 2025年第1期79-98,共20页
Authentication is the most crucial aspect of security and a predominant measure employed in cybersecurity.Cloud computing provides a shared electronic device resource for users via the internet,and the authentication ... Authentication is the most crucial aspect of security and a predominant measure employed in cybersecurity.Cloud computing provides a shared electronic device resource for users via the internet,and the authentication techniques used must protect data from attacks.Previous approaches failed to resolve the challenge of making passwords secure,memorable,usable,and time-saving.Graphical Password(GP)is still not widely utilized in reality because consumers suffer from multiple login stages.This paper proposes an Indexed Choice-Based Graphical Password(ICGP)scheme for improving the authentication part.ICGP consists of two stages:registration and authentication.At the registration stage,the user registers his/her data user name a number called Index Number(IN),and chooses an image from a grid of images.After completing the registration,ICGP gives the user a random unique number(UNo)to be a user ID.At the authentication stage,the user chooses a different image from the grid based on the random appearance of the registered image dimensions on the grid plus the registered Index Number.ICGP password is a combination of three factors;user’s name,UNo,and any image.According to the experiments,the proposed ICGP has achieved great improvements when compared to prior methods.The ICGP has increased the possible password numbers from 9.77e+6 to 3.74e+30,the password space from 1.20e+34 to 1.37e+84,and decreased the password entropy from 7.16e−7 to 8.26e−30. 展开更多
关键词 Authentication graphical password indexed choice-based graphical password user image system user number index number password space password entropy
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Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models 认领 引用 被引量：9
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作者 Zheyi Chen Liuchang Xu +5 位作者 Hongting Zheng Luyao Chen Amr Tolba Liang Zhao Keping Yu Hailin Feng 《Computers, Materials & Continua》 SCIE EI 2024年第8期1753-1808,共56页
Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the ... Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field. 展开更多
关键词 Artificial intelligence large language models large multimodal models foundation models
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LogUAD: Log Unsupervised Anomaly Detection Based on Word2Vec 认领 引用 被引量：8
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作者 Jin Wang Changqing Zhao +3 位作者 Shiming He Yu Gu Osama Alfarraj Ahed Abugabah 《Computer Systems Science & Engineering》 SCIE EI 2022年第6期1207-1222,共16页
System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significan... System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster. 展开更多
关键词 Log anomaly detection log instability word2Vec feature extraction
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Data Secure Storage Mechanism of Sensor Networks Based on Blockchain 认领 引用 被引量：9
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作者 Jin Wang Wencheng Chen +3 位作者 Lei Wang R.Simon Sherratt Osama Alfarraj Amr Tolba 《Computers, Materials & Continua》 SCIE EI 2020年第12期2365-2384,共20页
As the number of sensor network application scenarios continues to grow,the security problems inherent in this approach have become obstacles that hinder its wide application.However,it has attracted increasing attent... As the number of sensor network application scenarios continues to grow,the security problems inherent in this approach have become obstacles that hinder its wide application.However,it has attracted increasing attention from industry and academia.The blockchain is based on a distributed network and has the characteristics of non-tampering and traceability of block data.It is thus naturally able to solve the security problems of the sensor networks.Accordingly,this paper first analyzes the security risks associated with data storage in the sensor networks,then proposes using blockchain technology to ensure that data storage in the sensor networks is secure.In the traditional blockchain,the data layer uses a Merkle hash tree to store data;however,the Merkle hash tree cannot provide non-member proof,which makes it unable to resist the attacks of malicious nodes in networks.To solve this problem,this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and non-member proof.Moreover,the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion requirements.This paper therefore proposes a new type of unbounded accumulator and provides its definition and security model.Finally,this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance. 展开更多
关键词 Sensor networks blockchain unbounded accumulator storage mechanism
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A Storage Optimization Scheme for Blockchain Transaction Databases 认领 引用 被引量：11
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作者 Jingyu Zhang Siqi Zhong +2 位作者 Jin Wang Xiaofeng Yu Osama Alfarraj 《Computer Systems Science & Engineering》 SCIE EI 2021年第3期521-535,共15页
As the typical peer-to-peer distributed networks, blockchain systemsrequire each node to copy a complete transaction database, so as to ensure newtransactions can by verified independently. In a blockchain system (e.g... As the typical peer-to-peer distributed networks, blockchain systemsrequire each node to copy a complete transaction database, so as to ensure newtransactions can by verified independently. In a blockchain system (e.g., bitcoinsystem), the node does not rely on any central organization, and every node keepsan entire copy of the transaction database. However, this feature determines thatthe size of blockchain transaction database is growing rapidly. Therefore, with thecontinuous system operations, the node memory also needs to be expanded tosupport the system running. Especially in the big data era, the increasing networktraffic will lead to faster transaction growth rate. This paper analyzes blockchaintransaction databases and proposes a storage optimization scheme. The proposedscheme divides blockchain transaction database into cold zone and hot zone usingexpiration recognition method based on Least Recently Used (LRU) algorithm. Itcan achieve storage optimization by moving unspent transaction outputs outsidethe in-memory transaction databases. We present the theoretical analysis on theoptimization method to validate the effectiveness. Extensive experiments showour proposed method outperforms the current mechanism for the blockchaintransaction databases. 展开更多
关键词 Blockchain distributed systems transaction databases
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Digital Continuity Guarantee Approach of Electronic Record Based on Data Quality Theory 认领 引用 被引量：8
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作者 Yongjun Ren Jian Qi +2 位作者 Yaping Cheng Jin Wang Osama Alfarraj 《Computers, Materials & Continua》 SCIE EI 2020年第6期1471-1483,共13页
Since the British National Archive put forward the concept of the digital continuity in 2007,several developed countries have worked out their digital continuity action plan.However,the technologies of the digital con... Since the British National Archive put forward the concept of the digital continuity in 2007,several developed countries have worked out their digital continuity action plan.However,the technologies of the digital continuity guarantee are still lacked.At first,this paper analyzes the requirements of digital continuity guarantee for electronic record based on data quality theory,then points out the necessity of data quality guarantee for electronic record.Moreover,we convert the digital continuity guarantee of electronic record to ensure the consistency,completeness and timeliness of electronic record,and construct the first technology framework of the digital continuity guarantee for electronic record.Finally,the temporal functional dependencies technology is utilized to build the first integration method to insure the consistency,completeness and timeliness of electronic record. 展开更多
关键词 Electronic record digital continuity data quality
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TLSmell: Direct Identification on Malicious HTTPs Encryption Traffic withSimple Connection-Specific Indicators 认领 引用 被引量：5
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作者 Zhengqiu Weng Timing Chen +3 位作者 Tiantian Zhu Hang Dong Dan Zhou Osama Alfarraj 《Computer Systems Science & Engineering》 SCIE EI 2021年第4期105-119,共15页
Internet traffic encryption is a very common traffic protection method.Most internet traffic is protected by the encryption protocol called transport layersecurity (TLS). Although traffic encryption can ensure the sec... Internet traffic encryption is a very common traffic protection method.Most internet traffic is protected by the encryption protocol called transport layersecurity (TLS). Although traffic encryption can ensure the security of communication, it also enables malware to hide its information and avoid being detected.At present, most of the malicious traffic detection methods are aimed at the unencrypted ones. There are some problems in the detection of encrypted traffic, suchas high false positive rate, difficulty in feature extraction, and insufficient practicability. The accuracy and effectiveness of existing methods need to be improved.In this paper, we present TLSmell, a framework that conducts maliciousencrypted HTTPs traffic detection with simple connection-specific indicators byusing different classifiers based online training. We perform deep packet analysisof encrypted traffic through data pre-processing to extract effective features, andthen the online training algorithm is used for training and prediction. Withoutdecrypting the original traffic, high-precision malicious traffic detection and analysis are realized, which can guarantee user privacy and communication security.At the same time, since there is no need to decrypt the traffic in advance, the effi-ciency of detecting malicious HTTPs traffic will be greatly improved. Combinedwith the traditional detection and analysis methods, malicious HTTPs traffic isscreened, and suspicious traffic is further analyzed by the expert through the context of suspicious behaviors, thereby improving the overall performance of malicious encrypted traffic detection. 展开更多
关键词 Cyber security malware detection TLS feature engineering
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EBAKE-SE: A novel ECC-based authenticated key exchange between industrial IoT devices using secure element 认领 引用 被引量：4
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作者 Chintan Patel Ali Kashif Bashir +1 位作者 Ahmad Ali AlZubi Rutvij Jhaveri 《Digital Communications and Networks》 SCIE CSCD 2023年第2期358-366,共9页
Industrial IoT(IIoT)aims to enhance services provided by various industries,such as manufacturing and product processing.IIoT suffers from various challenges,and security is one of the key challenge among those challe... Industrial IoT(IIoT)aims to enhance services provided by various industries,such as manufacturing and product processing.IIoT suffers from various challenges,and security is one of the key challenge among those challenges.Authentication and access control are two notable challenges for any IIoT based industrial deployment.Any IoT based Industry 4.0 enterprise designs networks between hundreds of tiny devices such as sensors,actuators,fog devices and gateways.Thus,articulating a secure authentication protocol between sensing devices or a sensing device and user devices is an essential step in IoT security.In this paper,first,we present cryptanalysis for the certificate-based scheme proposed for a similar environment by Das et al.and prove that their scheme is vulnerable to various traditional attacks such as device anonymity,MITM,and DoS.We then put forward an interdevice authentication scheme using an ECC(Elliptic Curve Cryptography)that is highly secure and lightweight compared to other existing schemes for a similar environment.Furthermore,we set forth a formal security analysis using the random oracle-based ROR model and informal security analysis over the Doleve-Yao channel.In this paper,we present comparison of the proposed scheme with existing schemes based on communication cost,computation cost and security index to prove that the proposed EBAKE-SE is highly efficient,reliable,and trustworthy compared to other existing schemes for an inter-device authentication.At long last,we present an implementation for the proposed EBAKE-SE using MQTT protocol. 展开更多
关键词 Internet of things Authentication Elliptic curve cryptography Secure key exchange Message Queuing telemetry transport
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification 认领 引用 被引量：2
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作者 Noureen Talpur Said Jadid Abdulkadir +2 位作者 Mohd Hilmi Hasan Hitham Alhussian Ayed Alwadain 《Computers, Materials & Continua》 SCIE EI 2023年第3期5799-5820,共22页
Machine learning(ML)practices such as classification have played a very important role in classifying diseases in medical science.Since medical science is a sensitive field,the pre-processing of medical data requires ... Machine learning(ML)practices such as classification have played a very important role in classifying diseases in medical science.Since medical science is a sensitive field,the pre-processing of medical data requires careful handling to make quality clinical decisions.Generally,medical data is considered high-dimensional and complex data that contains many irrelevant and redundant features.These factors indirectly upset the disease prediction and classification accuracy of any ML model.To address this issue,various data pre-processing methods called Feature Selection(FS)techniques have been presented in the literature.However,the majority of such techniques frequently suffer from local minima issues due to large solution space.Thus,this study has proposed a novel wrapper-based Sand Cat SwarmOptimization(SCSO)technique as an FS approach to find optimum features from ten benchmark medical datasets.The SCSO algorithm replicates the hunting and searching strategies of the sand cat while having the advantage of avoiding local optima and finding the ideal solution with minimal control variables.Moreover,K-Nearest Neighbor(KNN)classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO algorithm.The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy,optimum feature size,and computational cost in seconds.The simulation results on the benchmark medical datasets revealed that the proposed SCSO-KNN approach has outperformed comparative algorithms with an average classification accuracy of 93.96%by selecting 14.2 features within 1.91 s.Additionally,the Wilcoxon rank test was used to perform the significance analysis between the proposed SCSOKNN method and six other algorithms for a p-value less than 5.00E-02.The findings revealed that the proposed algorithm produces better outcomes with an average p-value of 1.82E-02.Moreover,potential future directions are also suggested as a result of the study’s promising findings. 展开更多
关键词 Machine learning optimization feature selection classification medical data
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Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks 认领 引用 被引量：3
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作者 Atif Mehmood Ahed Abugabah +1 位作者 Ahmed Ali AlZubi Louis Sanzogni 《Computer Systems Science & Engineering》 SCIE EI 2022年第10期305-315,共11页
Alzheimer’s disease(AD)is a neurodegenerative disorder,causing the most common dementia in the elderly peoples.The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA.Magneti... Alzheimer’s disease(AD)is a neurodegenerative disorder,causing the most common dementia in the elderly peoples.The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA.Magnetic resonance imaging(MRI)is the leading modality used for the diagnosis of AD.Deep learning based approaches have produced impressive results in this domain.The early diagnosis of AD depends on the efficient use of classification approach.To address this issue,this study proposes a system using two convolutional neural networks(CNN)based approaches for an early diagnosis of AD automatically.In the proposed system,we use segmented MRI scans.Input data samples of three classes include 110 normal control(NC),110 mild cognitive impairment(MCI)and 105 AD subjects are used in this paper.The data is acquired from the ADNI database and gray matter(GM)images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models.The proposed approaches segregate among NC,MCI,and AD.While testing both methods applied on the segmented data samples,the highest performance results of the classification in terms of accuracy on NC vs.AD are 95.33%and 89.87%,respectively.The proposed methods distinguish between NC vs.MCI and MCI vs.AD patients with a classification accuracy of 90.74%and 86.69%.The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing. 展开更多
关键词 Alzheimer’s disease neural networks intelligent systems gray matter
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Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection 认领 引用 被引量：1
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作者 Qasem Al-Tashi Tareq M Shami +9 位作者 Said Jadid Abdulkadir Emelia Akashah Patah Akhir Ayed Alwadain Hitham Alhussain Alawi Alqushaibi Helmi MD Rais Amgad Muneer Maliazurina B.Saad Jia Wu Seyedali Mirjalili 《Computer Systems Science & Engineering》 SCIE EI 2023年第11期1937-1966,共30页
The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective ... The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost. 展开更多
关键词 Feature selection multi-objective optimization grey wolf optimizer Lévy flight mutation classification
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A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images 认领 引用 被引量：1
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作者 Nasser Alalwan Ahmed I.Taloba +2 位作者 Amr Abozeid Ahmed Ibrahim Alzahrani Ali H.Al-Bayatti 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第3期2497-2517,共21页
An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and s... An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been devised.To date,several methods have been employed to identify pneumonia.The Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology.However,these methods are complex,inefficient,and imprecise to analyze a big number of datasets.In this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is proposed.The proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and accuracy.The Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)approach.Relevant features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of Pneumonia.Experimental results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification tasks.The proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively. 展开更多
关键词 African buffalo optimization convolutional neural network pneumonia X-ray
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DoS Attack Detection Based on Deep Factorization Machine in SDN 认领 引用 被引量：1
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作者 Jing Wang Xiangyu Lei +3 位作者 Qisheng Jiang Osama Alfarraj Amr Tolba Gwang-jun Kim 《Computer Systems Science & Engineering》 SCIE EI 2023年第5期1727-1742,共16页
Software-Defined Network(SDN)decouples the control plane of network devices from the data plane.While alleviating the problems presented in traditional network architectures,it also brings potential security risks,par... Software-Defined Network(SDN)decouples the control plane of network devices from the data plane.While alleviating the problems presented in traditional network architectures,it also brings potential security risks,particularly network Denial-of-Service(DoS)attacks.While many research efforts have been devoted to identifying new features for DoS attack detection,detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks.To solve this problem,a new method of DoS attack detection based on Deep Factorization Machine(DeepFM)is proposed in SDN.Firstly,we select the Growth Rate of Max Matched Packets(GRMMP)in SDN as detection feature.Then,the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS attacks.After training,the model can be used to infer whether SDN is under DoS attacks,and a DeepFM-based detection method for DoS attacks against client host is implemented.Simulation results show that our method can effectively detect DoS attacks in SDN.Compared with the K-Nearest Neighbor(K-NN),Artificial Neural Network(ANN)models,Support Vector Machine(SVM)and Random Forest models,our proposed method outperforms in accuracy,precision and F1 values. 展开更多
关键词 Software-defined network denial-of-service attacks deep factorization machine GRMMP
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