Journal of King Saud University-Computer and Information Sciences最新文献

{"title":"Face forgery video detection based on expression key sequences","authors":"Yameng Tu,&nbsp;Jianbin Wu,&nbsp;Liang Lu,&nbsp;Shuaikang Gao,&nbsp;MingHao Li","doi":"10.1016/j.jksuci.2024.102142","DOIUrl":"10.1016/j.jksuci.2024.102142","url":null,"abstract":"<div><p>In order to minimize additional computational costs in detecting forged videos, and enhance detection accuracy, this paper employs dynamic facial expression sequences as key sequences, replacing original video sequences as inputs for the detection model. A spatio-temporal dual-branch detection network is designed based on the visual Transformer architecture. Specifically, this process involves three steps. Firstly, dynamic facial expression sequences are localized as key sequences using optical flow difference algorithms. Subsequently, the spatial branch network employs the focal self-attention mechanism to focus on dynamic features of expression-relevant regions and uses Factorization Machines to facilitate feature interaction among multiple key sequences. Meanwhile, the temporal branch network concentrates on learning the temporal inconsistency of optical flow differences between adjacent frames. Finally, a binary classification linear SVM combines the Softmax values from the two branch networks to provide the ultimate detection outcome. Experimental results on the Faceforensics++ dataset demonstrate: (a) replacing whole video sequences with facial expression key sequences effectively reduces training and detection time by nearly 80% and 90%, respectively; (b) compared to state-of-the-art methods involving random sequence/frame extraction and key frame extraction based on video compression techniques, the proposed approach in this paper presents a more competitive detection accuracy.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102142"},"PeriodicalIF":5.2,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002313/pdfft?md5=d3161c3d47c3e55bf622551f8213c551&pid=1-s2.0-S1319157824002313-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review on software reliability prediction via swarm intelligence algorithms","authors":"Li Sheng Kong ,&nbsp;Muhammed Basheer Jasser ,&nbsp;Samuel-Soma M. Ajibade ,&nbsp;Ali Wagdy Mohamed","doi":"10.1016/j.jksuci.2024.102132","DOIUrl":"10.1016/j.jksuci.2024.102132","url":null,"abstract":"<div><p>The widespread integration of software into all parts of our lives has led to the need for software of higher reliability. Ensuring reliable software usually necessitates some form of formal methods in the early stages of the development process which requires strenuous effort. Hence, researchers in the field of software reliability introduced Software Reliability Growth Models (SRGMs) as a relatively inexpensive approach to software reliability prediction. Conventional parameter estimation methods of SRGMs were ineffective and left more to be desired. Consequently, researchers sought out swarm intelligence to combat its flaws, resulting in significant improvements. While similar surveys exist within the domain, the surveys are broader in scope and do not cover many swarm intelligence algorithms. Moreover, the broader scope has resulted in the occasional omission of information regarding the design for reliability predictions. A more comprehensive survey containing 38 studies and 18 different swarm intelligence algorithms in the domain is presented. Each design proposed by the studies was systematically analyzed where relevant information including the measures used, datasets used, SRGMs used, and the effectiveness of each proposed design, were extracted and organized into tables and taxonomies to be able to identify the current trends within the domain. Some notable findings include the distance-based approach providing a high prediction accuracy and an increasing trend in hybridized variants of swarm intelligence algorithms designs to predict software reliability. Future researchers are encouraged to include Mean Square Error (MSE) or Root MSE as the measures offer the largest sample size for comparison.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102132"},"PeriodicalIF":5.2,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002210/pdfft?md5=65281963d468eb6753881c759697abc2&pid=1-s2.0-S1319157824002210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A many-to-many matching with externalities solution for parallel task offloading in IoT networks","authors":"Usman Mahmood Malik ,&nbsp;Muhammad Awais Javed ,&nbsp;Abdulaziz AlMohimeed ,&nbsp;Mohammed Alkhathami ,&nbsp;Deafallah Alsadie ,&nbsp;Abeer Almujalli","doi":"10.1016/j.jksuci.2024.102134","DOIUrl":"10.1016/j.jksuci.2024.102134","url":null,"abstract":"<div><p>The efficient and timely execution of tasks is a fundamental challenge in the realm of future Internet of Things (IoT) networks. To address this challenge, fog devices are often deployed close to end devices to facilitate task processing on behalf of IoT nodes. One strategy for improving task computational delay is to employ parallel task offloading, in which tasks are subdivided into subtasks and sent to different fog devices for execution in parallel. However, allocating computational resources to fog nodes and mapping these resources to IoT subtasks is a key challenge in this area. This work models the parallel task offloading problem as a graph-matching problem and utilizes a many-to-many matching technique to achieve a stable mapping of IoT subtasks to fog node resources. Unfortunately, the proposed solution is subject to the problem of externalities due to the dynamic preference profiling of fog nodes. To address this issue, we employ an iterative algorithm to resolve any blocking pairs that may arise. Our results demonstrate that the proposed technique reduces the task latency by 29% as compared to other matching-based techniques available in the literature.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102134"},"PeriodicalIF":5.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002234/pdfft?md5=ca723de57705f68d89bad154b59605a4&pid=1-s2.0-S1319157824002234-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141960831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel image captioning model with visual-semantic similarities and visual representations re-weighting","authors":"Alaa Thobhani ,&nbsp;Beiji Zou ,&nbsp;Xiaoyan Kui ,&nbsp;Asma A. Al-Shargabi ,&nbsp;Zaid Derea ,&nbsp;Amr Abdussalam ,&nbsp;Mohammed A. Asham","doi":"10.1016/j.jksuci.2024.102127","DOIUrl":"10.1016/j.jksuci.2024.102127","url":null,"abstract":"<div><p>Image captioning, the task of generating descriptive sentences for images, has seen significant advancements by incorporating semantic information. However, previous studies employed semantic attribute detectors to extract predetermined attributes consistently applied at every time step, resulting in the use of irrelevant attributes to the linguistic context during words’ generation. Furthermore, the integration between semantic attributes and visual representations in previous works is considered superficial and ineffective, leading to the neglection of the rich visual-semantic connections affecting the captioning model’s performance. To address the limitations of previous models, we introduced a novel framework that adapts attribute usage based on contextual relevance and effectively utilizes the similarities between visual features and semantic attributes. Our framework includes an Attribute Detection Component (ADC) that predicts relevant attributes using visual features and attribute embeddings. The Attribute Prediction and Visual Weighting module (APVW) then dynamically adjusts these attributes and generates weights to refine the visual context vector, enhancing semantic alignment. Our approach demonstrated an average improvement of 3.30% in BLEU@1 and 5.24% in CIDEr on MS-COCO, and 6.55% in BLEU@1 and 25.72% in CIDEr on Flickr30K, during CIDEr optimization phase.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102127"},"PeriodicalIF":5.2,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002167/pdfft?md5=a64ddf3f2ec61fdc99923155773d0fc6&pid=1-s2.0-S1319157824002167-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel deep CNN model with entropy coded sine cosine for corn disease classification","authors":"Mehak Mushtaq Malik ,&nbsp;Abdul Muiz Fayyaz ,&nbsp;Mussarat Yasmin ,&nbsp;Said Jadid Abdulkadir ,&nbsp;Safwan Mahmood Al-Selwi ,&nbsp;Mudassar Raza ,&nbsp;Sadia Waheed","doi":"10.1016/j.jksuci.2024.102126","DOIUrl":"10.1016/j.jksuci.2024.102126","url":null,"abstract":"<div><p>Corn diseases significantly impact crop yields, posing a major challenge to agricultural productivity. Early and accurate detection of these diseases is crucial for effective management and mitigation. Existing methods, mostly relying on analyzing corn leaves, often lack the precision to identify and classify a wide range of diseases under varying conditions. This study introduces a novel approach to detecting corn diseases using image processing and deep learning techniques, aiming to enhance detection accuracy through pre-processing, improved feature extraction and selection, and classification algorithms. A new deep Convolutional Neural Network (CNN) model named TreeNet, with 35 layers and 38 connections, is proposed. TreeNet is pre-trained using the Plant Village imaging dataset. For image pre-processing, the YCbCr color space is utilized to improve color representation and contrast. Feature extraction is performed using TreeNet and two pre-trained models, Darknet-53, and DenseNet-201, with features fused using a serial-based fusion method. The Entropy-coded Sine Cosine Algorithm is applied for feature selection, optimizing the feature set for classification. The selected features are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers, with extensive experiments conducted using both 5-fold and 10-fold cross-validation, and feature sizes ranging from 200 to 1150. The proposed method achieves classification accuracy, precision, recall, and F1-score of 99.8%, 99%, 100%, and 99%, respectively, surpassing existing benchmarks. The integration of TreeNet with Darknet-53 and DenseNet-201, along with robust pre-processing and feature selection, significantly improves corn disease detection, highlighting the potential of advanced CNN architectures in agriculture.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102126"},"PeriodicalIF":5.2,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002155/pdfft?md5=43529fffcaeee3e790d439f86b92c4d2&pid=1-s2.0-S1319157824002155-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141704359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG signal fusion reconstruction via hash autoencoder and margin semantic reinforcement","authors":"Yixian Fang ,&nbsp;Canwei Wang ,&nbsp;Yuwei Ren ,&nbsp;Fangzhou Xu","doi":"10.1016/j.jksuci.2024.102124","DOIUrl":"10.1016/j.jksuci.2024.102124","url":null,"abstract":"<div><p>The ECG signal is often accompanied by noise, which can affect its shape characteristics, so it is important to perform signal de-noising. However, the commonly used signal noise reduction methods, such as wavelet or filter transformation, often prioritize high-frequency signals over low-frequency ones, leading to the loss of low-frequency band features or difficulties in capturing them. We propose a fusion reconstruction framework that combines hash autoencoder and margin semantic reinforcement to enhance low-frequency band features. Specifically, for labeled samples, margin semantic reinforcement identifies and corrects weight discrepancies among bands with similar waveforms but different labels to amplify the low-frequency signals associated with the label and reduce irrelevant ones. Meanwhile, hash autoencoder utilizes a semantic hash dictionary to reconstruct the original signal and mitigate noise pollution. For unlabeled samples, the hash autoencoder is utilized to generate pseudo-labels, followed by the reproduction of the aforementioned enhanced reconstruction process. The final step involves weighting the two types of signals, enhanced with margin semantics and hash autoencoder reconstruction, to achieve the reconstruction objective of the original signal, facilitating recognition and detection tasks. Experiments conducted on different classical classifiers demonstrate that the reconstructed ECG signals can significantly improve their performance.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 6","pages":"Article 102124"},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002131/pdfft?md5=cb705ac9ed204e1395389a7ec4365e45&pid=1-s2.0-S1319157824002131-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TortoiseBFT: An asynchronous consensus algorithm for IoT system","authors":"Yang Liu ,&nbsp;Jianhao Fu ,&nbsp;Miaomiao Zhang ,&nbsp;Shidong Shi ,&nbsp;Jingwen Chen ,&nbsp;Song Peng ,&nbsp;Yaoqi Wang","doi":"10.1016/j.jksuci.2024.102104","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102104","url":null,"abstract":"<div><p>Traditional partial synchronous Byzantine fault tolerant (BFT) protocols are confronted with new challenges when applied to large-scale networks like IoT systems, which bring about rigorous demand for the liveness and consensus efficiency of BFT protocols in asynchronous network environments. HoneyBadgerBFT is the first practical asynchronous BFT protocol, which employs a reliable broadcast protocol (RBC) to broadcast transactions and an asynchronous binary agreement protocol (ABA) to determine whether transactions should be committed. DumboBFT is a follow-up proposal that requires fewer instances of ABA and achieves higher throughput than HoneyBadgerBFT, but it does not optimize the communication overhead of HoneyBadgerBFT.</p><p>In this paper, we propose TortoiseBFT, a high-performance asynchronous BFT protocol with three stages. We can significantly reduce communication overhead by determining the order of transactions first and requesting missing transactions after. Our two-phase transaction recovery mechanism enables nodes to recover missing transactions by seeking help from <span><math><mrow><mn>2</mn><mi>f</mi><mo>+</mo><mn>1</mn></mrow></math></span> nodes. To improve the overall throughput of the system, we lower the verification overhead of threshold signatures in HoneyBadgerBFT, DumboBFT, and DispersedLedger from <span><math><mrow><mi>O</mi><mfenced><mrow><msup><mrow><mi>n</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></mfenced></mrow></math></span> to <span><math><mrow><mi>O</mi><mfenced><mrow><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></mfenced></mrow></math></span>. We develop a node reputation model that selects producers with stable network conditions, which helps to reduce the number of random lotteries. Experimental results show that TortoiseBFT improves system throughput, reduces transaction delays, and minimizes communication overhead compared to HoneyBadgerBFT, DumboBFT, and DispersedLedger.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 6","pages":"Article 102104"},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824001939/pdfft?md5=b62b8656b909e9140f73f1274436f4cf&pid=1-s2.0-S1319157824001939-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CoD-DSSE: A practical efficient dynamic searchable symmetric encryption with lightweight clients","authors":"Ze Zhu,&nbsp;Wanshan Xu,&nbsp;Junfeng Xu","doi":"10.1016/j.jksuci.2024.102106","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102106","url":null,"abstract":"<div><p>Dynamic searchable symmetric encryption(DSSE) combines dynamic update with searchable encryption, allowing users to not only achieve keyword retrieval, but also dynamically update encrypted data stored on semi-trusted cloud server, effectively protecting user’s privacy. However, the majority of existing DSSE schemes exhibit inefficiencies in practical applications because of their complex structure. In addition, to store the status of keywords, the storage requirements of the client increase proportionally with the number of keyword/document pairs. Therefore, the client storage will be overwhelmed when confronted with a substantial increase in the volume of keyword/document pairs. To solve these issues, we propose a practical efficient dynamic searchable symmetric encryption scheme with lightweight clients—CoD-DSSE. A novel index structure similar to a chest of drawers is proposed in CoD-DSSE, which allows users to efficiently search all document indexes through XOR operations and keeps the keyword status on the server to lightweight clients. Furthermore, we use a random number generator to construct new search tokens for forward security and achieve backward security by using a Bloom filter to store the deleted document index, which can significantly reduce communication costs. The experimental and security analyses show that CoD-DSSE is efficient and secure in practice.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 6","pages":"Article 102106"},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824001952/pdfft?md5=6ad7ea6cdc4037881ef06012ec4ce876&pid=1-s2.0-S1319157824001952-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ORD-WM: A two-stage loop closure detection algorithm for dense scenes","authors":"Chengze Wang ,&nbsp;Wei Zhou ,&nbsp;Gang Wang","doi":"10.1016/j.jksuci.2024.102115","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102115","url":null,"abstract":"<div><p>Loop closure detection is a crucial technique supporting localization and navigation in autonomous vehicles. Existing research focuses on feature extraction in global scenes while neglecting considerations for local dense environments. In such local scenes, there are a large number of buildings, vehicles, and traffic signs, characterized by abundant objects, dense distribution, and interlaced near and far. The current methods only employ a single strategy for constructing descriptors, which fails to provide a detailed representation of the feature distribution in dense scenes, leading to inadequate discrimination of descriptors. Therefore, this paper proposes a multi-information point cloud descriptor to address the aforementioned issues. This descriptor integrates three types of environmental features: object density, region density, and distance, enhancing the recognition capability in local dense scenes. Additionally, we incorporated wavelet transforms and invariant moments from the image domain, designing wavelet invariant moments with rotation and translation invariance. This approach resolves the issue of point cloud mismatch caused by LiDAR viewpoint variations. In the experimental part, We collected data from dense scenes and conducted targeted experiments, demonstrating that our method achieves excellent loop closure detection performance in these scenes. Finally, the method is applied to a complete SLAM system, achieving accurate mapping.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 6","pages":"Article 102115"},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002040/pdfft?md5=46360415eb85c7c1fd6d73aa79f22586&pid=1-s2.0-S1319157824002040-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141595577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Binary metaheuristic algorithms for 0–1 knapsack problems: Performance analysis, hybrid variants, and real-world application","authors":"Mohamed Abdel-Basset ,&nbsp;Reda Mohamed ,&nbsp;Safaa Saber ,&nbsp;Ibrahim M. Hezam ,&nbsp;Karam M. Sallam ,&nbsp;Ibrahim A. Hameed","doi":"10.1016/j.jksuci.2024.102093","DOIUrl":"10.1016/j.jksuci.2024.102093","url":null,"abstract":"<div><p>This paper examines the performance of three binary metaheuristic algorithms when applied to two distinct knapsack problems (0–1 knapsack problems (KP01) and multidimensional knapsack problems (MKP)). These binary algorithms are based on the classical mantis search algorithm (MSA), the classical quadratic interpolation optimization (QIO) method, and the well-known differential evolution (DE). Because these algorithms were designed for continuous optimization problems, they could not be used directly to solve binary knapsack problems. As a result, the V-shaped and S-shaped transfer functions are used to propose binary variants of these algorithms, such as binary differential evolution (BDE), binary quadratic interpolation optimization (BQIO), and binary mantis search algorithm (BMSA). These binary variants are evaluated using various high-dimensional KP01 examples and compared to several classical metaheuristic techniques to determine their efficacy. To enhance the performance of those binary algorithms, they are combined with repair operator 2 (RO2) to offer better hybrid variants, namely HMSA, HQIO, and HDE. Those hybrid algorithms are evaluated using several medium- and large-scale KP01 and MKP instances, as well as compared to other hybrid algorithms, to demonstrate their effectiveness. This comparison is conducted using three performance metrics: average fitness value, Friedman mean rank, and computational cost. The experimental findings demonstrate that HQIO is a strong alternative for solving KP01 and MKP. In addition, the proposed algorithms are applied to the Merkle-Hellman Knapsack Cryptosystem and the resource allocation problem in adaptive multimedia systems (AMS) to illustrate their effectiveness when applied to optimize those real applications. The experimental findings illustrate that the proposed HQIO is a strong alternative for handling various knapsack-based applications.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 6","pages":"Article 102093"},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824001824/pdfft?md5=eecc477cb95abd1cca413bd63da31783&pid=1-s2.0-S1319157824001824-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141403813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}