PeerJ Computer Science最新文献

{"title":"Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia.","authors":"Mosleh Hmoud Al-Adhaileh,&nbsp;Theyazn H H Aldhyani","doi":"10.7717/peerj-cs.1104","DOIUrl":"https://doi.org/10.7717/peerj-cs.1104","url":null,"abstract":"<p><p>Predicting crop yields is a critical issue in agricultural production optimization and intensification research. Accurate foresights of natural circumstances a year in advance can have a considerable impact on management decisions regarding crop selection, rotational location in crop rotations, agrotechnical methods employed, and long-term land use planning. One of the most important aspects of precision farming is sustainability. The novelty of this study is to evidence the effective of the temperature, pesticides, and rainfall environment parameters in the influence sustainable agriculture and economic efficiency at the farm level in Saudi Arabia. Furthermore, predicting the future values of main crop yield in Saudi Arabia. The use of artificial intelligence (AI) to estimate the impact of environment factors and agrotechnical parameters on agricultural crop yields and to anticipate yields is examined in this study. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model was built to accurately predict the crop yield, temperature, insecticides, and rainfall based on environmental data. The dataset is collected from different Saudi Arabia regions from 1994 to 2016, including the temperature, insecticides, rainfall, and crop yields for potatoes, rice, sorghum, and wheat. For this study, we relied on five different statistical evaluation metrics: the mean square error (MSE), the root-mean-square error (RMSE), normalized root mean square error (NRMSE), Pearson's correlation coefficient (<i>R</i>%), and the determination coefficient (<i>R</i> ²). Analyses of datasets for crop yields, temperature, and insecticides led to the development of the MLP models. The datasets are randomly divided into separate samples, 70% for training and 30% for testing. The best-performing MLP model is characterized by values of (<i>R</i> = 100%) and (<i>R</i> ² = 96.33) for predicting insecticides in the testing process. The temperature, insecticides, and rainfall were examined with different crop yields to confirm the effectiveness of these parameters for increasing product crop yields in Saudi Arabia; we found that these items had highest relationships. The average values are <i>R</i> = 98.20%, 96.50, and 99.14% with for the temperature, insecticides, and rainfall, respectively. Based on these findings, it appeared that each of the parameter categories that are considered (temperature, pesticides, and rainfall) had a similar contribution to the accuracy of anticipated yield projection.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1104"},"PeriodicalIF":3.8,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40573314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata.","authors":"Houcemeddine Turki,&nbsp;Dariusz Jemielniak,&nbsp;Mohamed A Hadj Taieb,&nbsp;Jose E Labra Gayo,&nbsp;Mohamed Ben Aouicha,&nbsp;Mus'ab Banat,&nbsp;Thomas Shafee,&nbsp;Eric Prud'hommeaux,&nbsp;Tiago Lubiana,&nbsp;Diptanshu Das,&nbsp;Daniel Mietchen","doi":"10.7717/peerj-cs.1085","DOIUrl":"https://doi.org/10.7717/peerj-cs.1085","url":null,"abstract":"<p><p>Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1085"},"PeriodicalIF":3.8,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40668084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prescriptive process monitoring: <i>Quo vadis</i>?","authors":"Kateryna Kubrak, Fredrik Milani, Alexander Nolte, Marlon Dumas","doi":"10.7717/peerj-cs.1097","DOIUrl":"10.7717/peerj-cs.1097","url":null,"abstract":"<p><p>Prescriptive process monitoring methods seek to optimize a business process by recommending interventions at runtime to prevent negative outcomes or address poorly performing cases. In recent years, various prescriptive process monitoring methods have been proposed. This article studies existing methods in this field via a systematic literature review (SLR). In order to structure the field, this article proposes a framework for characterizing prescriptive process monitoring methods according to their performance objective, performance metrics, intervention types, modeling techniques, data inputs, and intervention policies. The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods. This article highlights the need to validate existing and new methods in real-world settings, extend the types of interventions beyond those related to the temporal and cost perspectives, and design policies that take into account causality and second-order effects.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1097"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40646376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a blockchain-based digitally secured model for the educational sector in Saudi Arabia toward digital transformation.","authors":"Someah Alangari,&nbsp;Saeed M Alshahrani,&nbsp;Nayyar Ahmed Khan,&nbsp;Abdulrahman Abdullah Alghamdi,&nbsp;Jameel Almalki,&nbsp;Waleed Al Shehri","doi":"10.7717/peerj-cs.1120","DOIUrl":"https://doi.org/10.7717/peerj-cs.1120","url":null,"abstract":"<p><p>New universities and educational organizations are increasing in Saudi Arabia with the increase in the need for high-quality education. This increased the need for a fast transformation to digitise the educational system in Saudi Arabia, which is one of the important pillars of the Saudi Vision 2030. The students who study in these organizations suffer the verification of academic records and other educational documents. Students who want to study at universities abroad also face the challenge of academic records and certificates verification. A secure, fast, and transparent model is required in the education sector in order to verify academic certificates issued by various educational organizations. Blockchain technology can be used with high data security to empower the educational sector of Saudi Arabia in the digital transformation and to help the educational organizations in verifying academic documents. In order to avoid any document fraud and forgery, along with the ease of verification of academic records and educational documents for the students. This research focuses on developing a model which will be helpful in achieving digital transformation in academic document verification by blockchain technology.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1120"},"PeriodicalIF":3.8,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40656166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 mass media coverage in English and public reactions: a West-East comparison <i>via</i> Facebook posts.","authors":"Ahmad R Pratama,&nbsp;Firman M Firmansyah","doi":"10.7717/peerj-cs.1111","DOIUrl":"https://doi.org/10.7717/peerj-cs.1111","url":null,"abstract":"<p><p>Newspapers and other mass media outlets are critical in shaping public opinion on a variety of contemporary issues, including the COVID-19 pandemic. This study examines how the pandemic is portrayed in the news and how the public reacted differently in the West and East using archival data from Facebook posts about COVID-19 news by English-language mass media between January 2020 and April 2022 (<i>N</i> = 711,646). Specifically, we employed the Valence Aware Dictionary and sEntiment Reasoner (Vader) to measure the news tone on each COVID-19 news item shared on Facebook by mass media outlets. In addition, we calculated a polarity score based on Facebook special reactions (<i>i.e</i>., love, angry, sad, wow, haha, and care) received by each post to measure public reactions toward it. We discovered that people in Western countries reacted significantly more negatively to COVID-19 news than their East counterparts, despite the fact that the news itself, in aggregate, generally contained a relatively similar level of neutral tone in both West and East media. The implications of these distinctions are discussed in greater detail.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1111"},"PeriodicalIF":3.8,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40573315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneous feature-aware Transformer-CNN coupling network for person re-identification.","authors":"Yanchao Li,&nbsp;Guoyun Lian,&nbsp;Wenyu Zhang,&nbsp;Guanglin Ma,&nbsp;Jin Ren,&nbsp;Jinfeng Yang","doi":"10.7717/peerj-cs.1098","DOIUrl":"https://doi.org/10.7717/peerj-cs.1098","url":null,"abstract":"<p><p>Person re-identification plays an important role in the construction of the smart city. A reliable person re-identification system relieves users from the inefficient work of identifying the specific individual from enormous numbers of photos or videos captured by different surveillance devices. The most existing methods either focus on local discriminative features without global contextual information or scatter global features while ignoring the local features, resulting in ineffective attention to irregular pedestrian zones. In this article, a novel Transformer-CNN Coupling Network (TCCNet) is proposed to capture the fluctuant body region features in a heterogeneous feature-aware manner. We employ two bridging modules, the Low-level Feature Coupling Module (LFCM) and the High-level Feature Coupling Module (HFCM), to improve the complementary characteristics of the hybrid network. It is significantly helpful to enhance the capacity to distinguish between foreground and background features, thereby reducing the unfavorable impact of cluttered backgrounds on person re-identification. Furthermore, the duplicate loss for the two branches is employed to incorporate semantic information from distant preferences of the two branches into the resulting person representation. The experiments on two large-scale person re-identification benchmarks demonstrate that the proposed TCCNet achieves competitive results compared with several state-of-the-art approaches. The mean Average Precision (mAP) and Rank-1 identification rate on the MSMT17 dataset achieve 66.9% and 84.5%, respectively.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1098"},"PeriodicalIF":3.8,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40573313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reaching for upper bound ROUGE score of extractive summarization methods.","authors":"Iskander Akhmetov,&nbsp;Rustam Mussabayev,&nbsp;Alexander Gelbukh","doi":"10.7717/peerj-cs.1103","DOIUrl":"https://doi.org/10.7717/peerj-cs.1103","url":null,"abstract":"<p><p>The extractive text summarization (ETS) method for finding the salient information from a text automatically uses the exact sentences from the source text. In this article, we answer the question of what quality of a summary we can achieve with ETS methods? To maximize the ROUGE-1 score, we used five approaches: (1) adapted reduced variable neighborhood search (RVNS), (2) Greedy algorithm, (3) VNS initialized by Greedy algorithm results, (4) genetic algorithm, and (5) genetic algorithm initialized by the Greedy algorithm results. Furthermore, we ran experiments on articles from the arXive dataset. As a result, we found 0.59 and 0.25 scores for ROUGE-1 and ROUGE-2, respectively achievable by the approach, where the genetic algorithm initialized by the Greedy algorithm results, which happens to yield the best results out of the tested approaches. Moreover, those scores appear to be higher than scores obtained by the current state-of-the-art text summarization models: the best score in the literature for ROUGE-1 on the same data set is 0.46. Therefore, we have room for the development of ETS methods, which are now undeservedly forgotten.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1103"},"PeriodicalIF":3.8,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575858/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40646378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MF-Storm: a maximum flow-based job scheduler for stream processing engines on computational clusters to increase throughput.","authors":"Asif Muhammad,&nbsp;Muhammad Abdul Qadir","doi":"10.7717/peerj-cs.1077","DOIUrl":"https://doi.org/10.7717/peerj-cs.1077","url":null,"abstract":"<p><strong>Background: </strong>A scheduling algorithm tries to schedule multiple computational tasks on a cluster of multiple computing nodes to maximize throughput with optimal utilization of computational and communicational resources. A Stream Processing Engine (SPE) is deployed to run streaming applications (computational tasks) on a computational cluster which helps execution and coordination of these applications. It is observed that there is a gap in the optimal mapping of a computational and communicational load of a streaming application on the underlying computational and communication power of the resources (cluster). Frequently communicated tasks are scheduled at different processing nodes with relatively slow communicating links. This increases network latency with a decrease in resource utilization. Hence, reduces the achieved throughput of the cluster significantly.</p><p><strong>Methods: </strong>MF-Storm, a max-flow min-cut based job scheduler is presented to achieve a near-optimum schedule to maximize throughput. It schedules a streaming application by considering the processing, communication demands, available computational and communicational resources in a heterogeneous cluster, dynamically with minimized scheduling cost. To keep the scheduling cost minimum, the scheduler is built in a pipeline with two major stages: in the first stage, the application's tasks graph is partitioned using the max-flow min-cut algorithm to minimize inter-partition traffic, and in the second stage, these partitions are assigned to computing nodes according to the computational power of the cluster's nodes.</p><p><strong>Results: </strong>Extensive experiments were done to evaluate the performance of MF-Storm using different topologies with multiple scenarios on a physical cluster implementation. Results showed on average 148% improvement in throughput with 30% less computational resources as compared to different state-of-the-art schedulers.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1077"},"PeriodicalIF":3.8,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40656165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers.","authors":"İsmail Atacak,&nbsp;Kazım Kılıç,&nbsp;İbrahim Alper Doğru","doi":"10.7717/peerj-cs.1092","DOIUrl":"https://doi.org/10.7717/peerj-cs.1092","url":null,"abstract":"<p><strong>Background: </strong>Android is the most widely used operating system all over the world. Due to its open nature, the Android operating system has become the target of malicious coders. Ensuring privacy and security is of great importance to Android users.</p><p><strong>Methods: </strong>In this study, a hybrid architecture is proposed for the detection of Android malware from the permission information of applications. The proposed architecture combines the feature extraction power of the convolutional neural network (CNN) architecture and the decision making capability of fuzzy logic. Our method extracts features from permission information with a small number of filters and convolutional layers, and also makes the feature size suitable for ANFIS input. In addition, it allows the permission information to affect the classification without being neglected. In the study, malware was obtained from two different sources and two different data sets were created. In the first dataset, Drebin was used for malware applications, and in the second dataset, CICMalDroid 2020 dataset was used for malware applications. For benign applications, the Google Play Store environment was used.</p><p><strong>Results: </strong>With the proposed method, 92% accuracy in the first data set and 92% <i>F</i>-score value in the weighted average was achieved. In the second data set, an accuracy of 94.6% and an <i>F</i>-score of 94.6% on the weighted average were achieved. The results obtained in the study show that the proposed method outperforms both classical machine learning algorithms and fuzzy logic-based studies.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1092"},"PeriodicalIF":3.8,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40558136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FCMpy: a python module for constructing and analyzing fuzzy cognitive maps.","authors":"Samvel Mkhitaryan,&nbsp;Philippe Giabbanelli,&nbsp;Maciej K Wozniak,&nbsp;Gonzalo Nápoles,&nbsp;Nanne De Vries,&nbsp;Rik Crutzen","doi":"10.7717/peerj-cs.1078","DOIUrl":"https://doi.org/10.7717/peerj-cs.1078","url":null,"abstract":"<p><p>FCMpy is an open-source Python module for building and analyzing Fuzzy Cognitive Maps (FCMs). The module provides tools for end-to-end projects involving FCMs. It is able to derive fuzzy causal weights from qualitative data or simulating the system behavior. Additionally, it includes machine learning algorithms (<i>e.g</i>., Nonlinear Hebbian Learning, Active Hebbian Learning, Genetic Algorithms, and Deterministic Learning) to adjust the FCM causal weight matrix and to solve classification problems. Finally, users can easily implement scenario analysis by simulating hypothetical interventions (<i>i.e</i>., analyzing what-if scenarios). FCMpy is the first open-source module that contains all the functionalities necessary for FCM oriented projects. This work aims to enable researchers from different areas, such as psychology, cognitive science, or engineering, to easily and efficiently develop and test their FCM models without the need for extensive programming knowledge.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1078"},"PeriodicalIF":3.8,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40644336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}