Web Intelligence最新文献_第2页

Supply chain management with secured data transmission via improved DNA cryptosystem 通过改进的 DNA 密码系统进行安全数据传输的供应链管理

IF 0.3

Web Intelligence Pub Date : 2023-12-12 DOI: 10.3233/web-230105

P. Lahane, Shivaji R. Lahane

{"title":"Supply chain management with secured data transmission via improved DNA cryptosystem","authors":"P. Lahane, Shivaji R. Lahane","doi":"10.3233/web-230105","DOIUrl":"https://doi.org/10.3233/web-230105","url":null,"abstract":"Supply chain management (SCM) is most significant place of concentration in various corporate circumstances. SCM has both designed and monitored numerous tasks with the following phases such as allocation, creation, product sourcing, and warehousing. Based on this perspective, the privacy of data flow is more important among producers, suppliers, and customers to ensure the responsibility of the market. This work aims to develop a novel Improved Digital Navigator Assessment (DNA)-based Self Improved Pelican Optimization Algorithm (IDNA-based SIPOA model) for secured data transmission in SCM via blockchain. An improved DNA cryptosystem is done for the process of preservation for data. The original message is encrypted by Improved Advanced Encryption Standard (IAES). The optimal key generation is done by the proposed SIPOA algorithm. The efficiency of the adopted model has been analyzed with conventional methods with regard to security for secured data exchange in SCM. The proposed IDNA-based SIPOA obtained the lowest value for the 40% cypher text is 0.71, while the BWO is 0.79, DOA is 0.77, TWOA is 0.84, BOA is 0.83, POA is 0.86, SDSM is 0.88, DNASF is 0.82 and FSA-SLnO is 0.78, respectively.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139008870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid deep model for predicting anti-cancer drug efficacy in colorectal cancer patients 预测结直肠癌患者抗癌药物疗效的混合深度模型

IF 0.3

Web Intelligence Pub Date : 2023-12-08 DOI: 10.3233/web-230260

A. Karthikeyan, S. Jothilakshmi, S. Suthir

{"title":"Hybrid deep model for predicting anti-cancer drug efficacy in colorectal cancer patients","authors":"A. Karthikeyan, S. Jothilakshmi, S. Suthir","doi":"10.3233/web-230260","DOIUrl":"https://doi.org/10.3233/web-230260","url":null,"abstract":"Cancers are genetically diversified, so anticancer treatments have different levels of efficacy on people due to genetic differences. The main objective of this work is to predict the anticancer drug efficiency for colorectal cancer patients to reduce the mortality rates and provides immune energy for the patients. This paper proposes a novel anti-cancer drug efficacy system in colorectal cancer patients. The input data gene is normalized with the Min–Max normalization technique that normalizes the data in distinct scales. Subsequently, proposes an improved entropy-based feature to evaluate the uncertainty distribution of data, in which it induces weight to overcome the issue of computational complexity. Along with this feature, a correlation-based feature and statistical features are also retrieved. Subsequently, proposes a Recursive Feature Elimination with Hybrid Machine Learning (RFEHML) mechanism for selecting the appropriate feature set by eliminating the recursive features with the aid of hybrid Machine Learning strategies that combine decision tree and logistic regression. Also, the Gini impurity is employed for ranking the feature and selecting the maximum importance score by eliminating the least acquired importance score. Further, proposes a hybrid model for predicting the drug efficiency with the trained feature set. The hybrid model comprises of Long Short-Term Memory (LSTM) and Updated Rectified Linear Unit-Deep Convolutional Neural Network (UReLU-DCNN) model, in which DCNN is modified by updating the activation function at the fully connected layer. Consequently, the learned feature predicts the drug efficacy of anti-cancer in colorectal cancer patients by determining whether the patient is a responder or non-responder of the drug. Finally, the performance of the proposed RFEHML model is compared with other traditional approaches. It is found that the developed method has higher accuracy for each learning percentage, with values of 60LP = 92.48%, 70LP = 94.28%, 80LP = 95.24%, and 90LP = 96.86%, respectively.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stock market prediction-COVID-19 scenario with lexicon-based approach 股票市场预测--基于词典方法的 COVID-19 方案

IF 0.3

Web Intelligence Pub Date : 2023-12-01 DOI: 10.3233/web-230092

Y. Ayyappa, A.P. Siva Kumar

{"title":"Stock market prediction-COVID-19 scenario with lexicon-based approach","authors":"Y. Ayyappa, A.P. Siva Kumar","doi":"10.3233/web-230092","DOIUrl":"https://doi.org/10.3233/web-230092","url":null,"abstract":"Stock market forecasting remains a difficult problem in the economics industry due to its incredible stochastic nature. The creation of such an expert system aids investors in making investment decisions about a certain company. Due to the complexity of the stock market, using a single data source is insufficient to accurately reflect all of the variables that influence stock fluctuations. However, predicting stock market movement is a challenging undertaking that requires extensive data analysis, particularly from a big data perspective. In order to address these problems and produce a feasible solution, appropriate statistical models and artificially intelligent algorithms are needed. This paper aims to propose a novel stock market prediction by the following four stages; they are, preprocessing, feature extraction, improved feature level fusion and prediction. The input data is first put through a preparation step in which stock, news, and Twitter data (related to the COVID-19 epidemic) are processed. Under the big data perspective, the input data is taken into account. These pre-processed data are then put through the feature extraction, The improved aspect-based lexicon generation, PMI, and n-gram-based features in this case are derived from the news and Twitter data, while technical indicator-based features are derived from the stock data. The improved feature-level fusion phase is then applied to the extracted features. The ensemble classifiers, which include DBN, CNN, and DRN, were proposed during the prediction phase. Additionally, a SI-MRFO model is suggested to enhance the efficiency of the prediction model by adjusting the best classifier weights. Finally, SI-MRFO model’s effectiveness compared to the existing models with regard to MAE, MAPE, MSE and MSLE. The SI-MRFO accomplished the minimal MAE rate for the 90th learning percentage is approximately 0.015 while other models acquire maximum ratings.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138622322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel authentication scheme for secure data sharing in IoT enabled agriculture 物联网农业数据安全共享的新型认证方案

IF 0.3

Web Intelligence Pub Date : 2023-11-28 DOI: 10.3233/web-230244

Arun A. Kumbi, M. Birje

{"title":"A novel authentication scheme for secure data sharing in IoT enabled agriculture","authors":"Arun A. Kumbi, M. Birje","doi":"10.3233/web-230244","DOIUrl":"https://doi.org/10.3233/web-230244","url":null,"abstract":"Now a days, the Internet of Things (IoT) plays a vital role in every industry including agriculture due to its widespread and easy integrations. The agricultural methods are incorporated with IoT technologies for significant growth in agricultural fields. IoT is utilized to support farmers in using their resources effectively and support decision-making systems with better field monitoring techniques. The data collected from IoT-based agricultural systems are highly vulnerable to attack, hence to address this issue it is necessary to employ an authentication scheme. In this paper, Auth Key_Deep Convolutional Neural Network (Auth Key_DCNN) is designed to promote secure data sharing in IoT-enabled agriculture systems. The different entities, namely sensors, Private Key Generator (PKG), controller, and data user are initially considered and the parameters are randomly initialized. The entities are registered and by using DCNN a secret key is generated in PKG. The encryption of transmitted data is performed in the data protection phase during the protection of data between the controller and the user. Additionally, the performance of the designed model is estimated, where the experimental results revealed that the Auth Key_DCNN model recorded superior performance with a minimal computational cost of 142.56, a memory usage of 49.5 MB, and a computational time of 1.34 sec.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139225993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep hybrid model for attack detection in IoT-fog architecture with improved feature set and optimal training 利用改进的特征集和优化的训练，在物联网-雾架构中建立深度混合模型进行攻击检测

IF 0.3

Web Intelligence Pub Date : 2023-11-27 DOI: 10.3233/web-230187

N. Pokale, Pooja Sharma, Deepak T. Mane

{"title":"Deep hybrid model for attack detection in IoT-fog architecture with improved feature set and optimal training","authors":"N. Pokale, Pooja Sharma, Deepak T. Mane","doi":"10.3233/web-230187","DOIUrl":"https://doi.org/10.3233/web-230187","url":null,"abstract":"IoT-Fog computing provides a wide range of services for end-based IoT systems. End IoT devices interface with cloud nodes and fog nodes to manage client tasks. Critical attacks like DDoS and other security risks are more likely to compromise IoT end devices while they are collecting data between the fog and the cloud layer. It’s important to find these network vulnerabilities early. By extracting features and placing the danger in the network, DL is crucial in predicting end-user behavior. However, deep learning cannot be carried out on Internet of Things devices because to their constrained calculation and storage capabilities. In this research, we suggest a three-stage Deep Hybrid Detection Model for Attack Detection in IoT-Fog Architecture. Improved Z-score normalization-based data preparation will be carried out in the initial step. On the basis of preprocessed data, features like IG, raw data, entropy, and enhanced MI are extracted in the second step. The collected characteristics are used as input to hybrid classifiers dubbed optimized Deep Maxout and Deep Belief Network (DBN) in the third step of the process to classify the assaults based on the input dataset. A hybrid optimization model called the BMUJFO (Blue Monkey Updated Jellyfish Optimization) technique is presented for the best Deep Maxout training. Additionally, the suggested model produced higher accuracy, precision, sensitivity, and specificity results, with values of 95.26 percent, 94.84%, 96.28%, and 97.84%, respectively.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An empirical study of various detection based techniques with divergent learning’s 基于发散学习的各种检测技术的实证研究

Web Intelligence Pub Date : 2023-10-27 DOI: 10.3233/web-230103

Bhagyashree Pramod Bendale, Swati Swati Dattatraya Shirke

引用次数: 0

Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model 多目标约束下软件故障检测与定位的测试套件优化:基于混合优化的模型

IF 0.3

Web Intelligence Pub Date : 2023-08-28 DOI: 10.3233/web-220131

Adline Freeda R, Selvi Rajendran P

{"title":"Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model","authors":"Adline Freeda R, Selvi Rajendran P","doi":"10.3233/web-220131","DOIUrl":"https://doi.org/10.3233/web-220131","url":null,"abstract":"Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73334772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Unique Approach for Performance Analysis of a Blockchain and Cryptocurrency based Carbon Footprint Reduction System 基于区块链和加密货币的碳足迹减少系统性能分析的独特方法

IF 0.3

Web Intelligence Pub Date : 2023-08-11 DOI: 10.3233/web-220049

Ankit Panch, Dr. Om Prakash Sharma

{"title":"A Unique Approach for Performance Analysis of a Blockchain and Cryptocurrency based Carbon Footprint Reduction System","authors":"Ankit Panch, Dr. Om Prakash Sharma","doi":"10.3233/web-220049","DOIUrl":"https://doi.org/10.3233/web-220049","url":null,"abstract":"Blockchain technology is commonly used as a replicated and distributed database in different areas. In this paper, a smart home blockchain network connects smart homes through smart devices for reducing carbon footprint and thereby earning bitcoin value in the network. The network is composed of different smart homes interconnected with smart devices. The user makes a transaction request through the network layer and matches the user’s activity with the reward table located at the incentive layer to estimate the bitcoin value. Furthermore, the miner verifies the transaction and sends the bitcoin value to the user, and adds the respective block to the network structure. The optimal parameter used to estimate the bitcoin value is computed using the proposed Improved Invasive Weed Mayfly Optimization (IIWMO) algorithm. The developed method attained higher performance with the metrics, like coins earned, Annual Carbon Reduction (ACR), and fitness as 0.00357BTC, 23.891, and 0.6618 for 200 users. For 200 users the fitness obtained by the proposed method is 14.41%, 16.68%, and 11.68% higher when compared to existing approaches namely, Without optimization, IIWO, and MA, respectively.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89899668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving 基于车道检测和交通标志检测的深度学习路径跟踪控制

IF 0.3

Web Intelligence Pub Date : 2023-08-07 DOI: 10.3233/web-230011

Swati Jaiswal, B. C. Mohan

{"title":"Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving","authors":"Swati Jaiswal, B. C. Mohan","doi":"10.3233/web-230011","DOIUrl":"https://doi.org/10.3233/web-230011","url":null,"abstract":"Automated vehicles are a significant advancement in transportation technique, which provides safe, sustainable, and reliable transport. Lane detection, maneuver forecasting, and traffic sign recognition are the fundamentals of automated vehicles. Hence, this research focuses on developing a dynamic real-time decision-making system to obtain an effective driving experience in autonomous vehicles with the advancement of deep learning techniques. The deep learning classifier such as deep convolutional neural network (Deep CNN), SegNet and are utilized in this research for traffic signal detection, road segmentation, and lane detection. The main highlight of the research relies on the proposed Finch Hunt optimization, which involves the hyperparameter tuning of a deep learning classifier. The proposed real-time decision-making system achieves 97.44% accuracy, 97.56% of sensitivity, and 97.83% of specificity. Further, the proposed segmentation model achieves the highest clustering accuracy with 90.37% and the proposed lane detection model attains the lowest mean absolute error, mean square error, and root mean error of 17.76%, 11.32%, and 5.66% respectively. The proposed road segmentation model exceeds all the competent models in terms of clustering accuracy. Finally, the proposed model provides a better output for lane detection with minimum error, when compared with the existing model.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85168967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-objective hybrid optimization for micro strip patch antenna design 微带贴片天线设计的多目标混合优化

IF 0.3

Web Intelligence Pub Date : 2023-08-02 DOI: 10.3233/web-220112

Samuyelu Bommu, R. R, Y. Chincholkar, U. L. Mohite

{"title":"Multi-objective hybrid optimization for micro strip patch antenna design","authors":"Samuyelu Bommu, R. R, Y. Chincholkar, U. L. Mohite","doi":"10.3233/web-220112","DOIUrl":"https://doi.org/10.3233/web-220112","url":null,"abstract":"Due to their low price, light weights, as well as simple installation, Micro strip Patch Antennas (MPAs) have been made to perform in a double and multi-band applications. The MP receiver is created with an Electromagnetic Band Gap (EBG) structure in order to decrease the micro strip patch cross-polarized radiation but also achieve the crucial radiation criteria. The polymeric liquid crystals substratum is employed to decrease raw material costs, and also the applicable shape framework are employed to enhance receiver execution. We have established a new optimization based method which has two operating stages. In the begining stage, we have designed a Micro strip patch antenna with certain parameters. Afterwards, these design parameters length, width, height, substrate thickness under area such as get optimized by the newly introduced Battle Royale Customized Spider Monkey Optimization (BRCSMO) algorithm in order to get an antenna with higher performance. We have evaluated the proposed method with regard to measures like receiver profit, productivity, bandwidth, decline loss as well as Total Active Reflection coefficient (TARC) and the outcomes showed that this proposed technique can offer superior outcomes than other approaches.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73920705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0