Int. J. Intell. Comput. Cybern.最新文献

{"title":"A comparative survey of SSVEP recognition algorithms based on template matching of training trials","authors":"Tian-jian Luo","doi":"10.1108/ijicc-01-2022-0002","DOIUrl":"https://doi.org/10.1108/ijicc-01-2022-0002","url":null,"abstract":"PurposeSteady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to its characteristics of high accuracy and information transfer rate (ITR). To recognize the SSVEP components in collected EEG trials, a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years. In this paper, a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.Design/methodology/approachTo survey and compare the recently proposed recognition algorithms for SSVEP, this paper regarded the conventional canonical correlated analysis (CCA) as the baseline, and selected individual template CCA (ITCCA), multi-set CCA (MsetCCA), task related component analysis (TRCA), latent common source extraction (LCSE) and a sum of squared correlation (SSCOR) for comparison.FindingsFor the horizontal comparative of the six surveyed recognition algorithms, this paper adopted the “Tsinghua JFPM-SSVEP” data set and compared the average recognition performance on such data set. The comparative contents including: recognition accuracy, ITR, correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation. Based on the optimal time duration of stimulus presentation, the author has also compared the efficiency of the six compared algorithms. To measure the influence of different parameters, the number of training trials, the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.Originality/valueBased on the comparative results, this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes, real-time and computational complexity. Finally, the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122366114","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}

{"title":"An improved evaporation rate water cycle algorithm for energy-efficient routing protocol in WSNs","authors":"Vimala Dayalan, Manikandan Kuppusamy","doi":"10.1108/ijicc-12-2021-0292","DOIUrl":"https://doi.org/10.1108/ijicc-12-2021-0292","url":null,"abstract":"PurposeThe paper aims to introduce an efficient routing algorithm for wireless sensor networks (WSNs). It proposes an improved evaporation rate water cycle (improved ER-WC) algorithm and outlining the systems performance in improving the energy efficiency of WSNs. The proposed technique mainly analyzes the clustering problem of WSNs when huge tasks are performed.Design/methodology/approachThis proposed improved ER-WC algorithm is used for analyzing various factors such as network cluster-head (CH) energy, CH location and CH density in improved ER-WCA. The proposed study will solve the energy efficiency and improve network throughput in WSNs.FindingsThis proposed work provides optimal clustering method for Fuzzy C-means (FCM) where efficiency is improved in WSNs. Empirical evaluations are conducted to find network lifespan, network throughput, total network residual energy and network stabilization.Research limitations/implicationsThe proposed improved ER-WC algorithm has some implications when different energy levels of node are used in WSNs.Practical implicationsThis research work analyzes the nodes’ energy and throughput by selecting correct CHs in intra-cluster communication. It can possibly analyze the factors such as CH location, network CH energy and CH density.Originality/valueThis proposed research work proves to be performing better for improving the network throughput and increases energy efficiency for WSNs.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131023119","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}

{"title":"GSA-based support vector neural network: a machine learning approach for crop prediction to provision sustainable farming","authors":"A. Ashwitha, C. Latha","doi":"10.1108/ijicc-12-2021-0300","DOIUrl":"https://doi.org/10.1108/ijicc-12-2021-0300","url":null,"abstract":"PurposeAutomated crop prediction is needed for the following reasons: First, agricultural yields were decided by a farmer's ability to work in a certain field and with a particular crop previously. They were not always able to predict the crop and its yield solely on that idea alone. Second, seed firms frequently monitor how well new plant varieties would grow in certain settings. Third, predicting agricultural production is critical for solving emerging food security concerns, especially in the face of global climate change. Accurate production forecasts not only assist farmers in making informed economic and management decisions but they also aid in the prevention of famine. This results in farming systems’ efficiency and productivity gains, as well as reduced risk from environmental factors.Design/methodology/approachThis research paper proposes a machine learning technique for effective autonomous crop and yield prediction, which makes use of solution encoding to create solutions randomly, and then for every generated solution, fitness is evaluated to meet highest accuracy. Major focus of the proposed work is to optimize the weight parameter in the input data. The algorithm continues until the optimal agent or optimal weight is selected, which contributes to maximum accuracy in automated crop prediction.FindingsPerformance of the proposed work is compared with different existing algorithms, such as Random Forest, support vector machine (SVM) and artificial neural network (ANN). The proposed method support vector neural network (SVNN) with gravitational search agent (GSA) is analysed based on different performance metrics, such as accuracy, sensitivity, specificity, CPU memory usage and training time, and maximum performance is determined.Research limitations/implicationsRather than real-time data collected by Internet of Things (IoT) devices, this research focuses solely on historical data; the proposed work does not impose IoT-based smart farming, which enhances the overall agriculture system by monitoring the field in real time. The present study only predicts the sort of crop to sow not crop production.Originality/valueThe paper proposes a novel optimization algorithm, which is based on the law of gravity and mass interactions. The search agents in the proposed algorithm are a cluster of weights that interact with one another using Newtonian gravity and motion principles. A comparison was made between the suggested method and various existing strategies. The obtained results confirm the high-performance in solving diverse nonlinear functions.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122854451","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}

{"title":"Integrated dominating and hit set-inspired unequal clustering-based data aggregation in wireless sensor networks","authors":"G. V. Selvi, V. Muthukumaran, A. C. Kaladevi, S. S. Kumar, B. Swapna","doi":"10.1108/ijicc-10-2021-0225","DOIUrl":"https://doi.org/10.1108/ijicc-10-2021-0225","url":null,"abstract":"PurposeIn wireless sensor networks, improving the network lifetime is considered as the prime objective that needs to be significantly addressed during data aggregation. Among the traditional data aggregation techniques, cluster-based dominating set algorithms are identified as more effective in aggregating data through cluster heads. But, the existing cluster-based dominating set algorithms suffer from a major drawback of energy deficiency when a large number of communicating nodes need to collaborate for transferring the aggregated data. Further, due to this reason, the energy of each communicating node is gradually decreased and the network lifetime is also decreased. To increase the lifetime of the network, the proposed algorithm uses two sets: Dominating set and hit set.Design/methodology/approachThe proposed algorithm uses two sets: Dominating set and hit set. The dominating set constructs an unequal clustering, and the hit set minimizes the number of communicating nodes by selecting the optimized cluster head for transferring the aggregated data to the base station. The simulation results also infer that the proposed optimized unequal clustering algorithm (OUCA) is greater in improving the network lifetime to a maximum amount of 22% than the existing cluster head selection approach considered for examination.FindingsIn this paper, lifetime of the network is prolonged by constructing an unequal cluster using the dominating set and electing an optimized cluster head using hit set. The dominator set chooses the dominator based on the remaining energy and its node degree of each node. The optimized cluster head is chosen by the hit set to minimize the number of communicating nodes in the network. The proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit set. The simulation result confirms that the proposed algorithm prolonging the lifetime of the network efficiently when compared with the existing algorithms.Originality/valueThe proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit sets. The simulation result confirms that the proposed algorithm is prolonging the lifetime of the network efficiently when compared with the existing algorithms.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126657249","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}

{"title":"Designing an optimized confidential-data management system using preeminent access-control and block-chain","authors":"K. Madhura, R. Mahalakshmi","doi":"10.1108/ijicc-12-2021-0295","DOIUrl":"https://doi.org/10.1108/ijicc-12-2021-0295","url":null,"abstract":"PurposeThe blockchain system is required for coordinating and managing the information across the organizations. The internal assurance of the information system is under threat and requires a defined and arbitrary system for protective information sharing. The primary analysis of defining and providing the access to the data or information is proposed in this article. The major challenge faced by organizations is providing and maintaining security to the cumulated data infrastructure, protecting the confidential data flow within the infrastructure and to ascertain ethical operations within the organization.Design/methodology/approachIn this paper, a top-down approach is utilized to solve the issues faced during confidential data storage, track and alert internal malignant access. The web content management system (WCMS) market is growing and the challenging desideratum of solving security issues are incrementing. This research proposes a top-down security implementation methodology which alters the access control of the organization running the WCMS, it is a highly secure access-control with activity tracker that records who did what activity and who is responsible for malignant access tracker.FindingsThe upgraded access control system is implemented utilizing the most influential blockchain technology where the hash engendered by the Inter Planetary File System (IPFS) cloud is stored on the block to implement a confidential data tracker; a supplemental level of security is integrated to the admin by verifying its account ID by storing it on to the astute contracts and applying two-step authentication. A caliber of security is integrated during storage by integrating RHS encryption.Research limitations/implicationsThis system is proposed for the confidential student data storage (for e.g. educational documents, marks cards etc.) and tracking maleficent access.Originality/valueThe upgraded access control system is implemented using the most influential blockchain technology where the hash generated by the IPFS cloud is stored on the block to implement a confidential data tracker, an additional level of security is added to the admin by verifying its account ID by storing it on to the smart contracts and applying two-step authentication.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127220956","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}

{"title":"Thinned array antenna synthesis using modified binary particle swarm optimization with minimization of sidelobes","authors":"Gayatri Allu, M. Kumar, A. Prasad","doi":"10.1108/ijicc-11-2021-0273","DOIUrl":"https://doi.org/10.1108/ijicc-11-2021-0273","url":null,"abstract":"PurposeThe purpose of this paper is to propose radiating system by avoiding electromagnetic interference in unwanted directions and to radiate the energy in the required direction with an optimization technique.Design/methodology/approachPractically, multiple, incompatible variables require concurrent boost on a synthesis of systematic antenna assemblage. The authors have worked out the main statistic penalty function to ensure all the restrictions. Here, MBPSO (Modified Binary Particle Swarm Optimization) is developed and introduced thin planar synthesis restriction. The sigmoid function is used to update the particle position. Different analytical demonstrations have been carried out, and the exhibited methods are predominant than the algorithms.FindingsA 20 × 10 planar antenna array is synthesized using modified BPSO. The authors have suppressed the PSLL in two principal planes and as well as in the entire f plane. Numerical results state that MBPSO outperforms the other binary BPSO, BCSO, ACO, RGA, GA optimization techniques. MBPSO achieved a −51.84 dB PSLL level, whereas BPSO achieved −48.57 dB with the same 50% thinning.Originality/valuePlanar array antenna formation is one of the most complex syntheses because the array gets filled with more antenna elements. The machine-like complication and implementation of such an antenna arrangement with a broad opening would be expensive. It is not easy to control the required radiation patterns shape by using a uniform feeding network. To get better flexibility for sustaining the sidelobe levelheaded along with consistent amplitude distribution. So as far as prominence has been given to the evolutionary algorithm, find an ideal solution for objective array combinational problems.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123436468","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}

{"title":"QCA with reversible arithmetic and logic unit for nanoelectronics applications","authors":"G. M. S. Latha, S. Rooban","doi":"10.1108/ijicc-10-2021-0241","DOIUrl":"https://doi.org/10.1108/ijicc-10-2021-0241","url":null,"abstract":"PurposeIn this research work, brief quantum-dot cellular automata (QCA) concepts are discussed through arithmetic and logic units. This work is most useful for nanoelectronic applications, VLSI industry mainly depends on this type of fault-tolerant QCA based arithmetic logic unit (ALU) design. The ALU design is mainly depending on set instructions and rules; these are maintained through low-power ultra-functional tricks only possible with QCA-based reversible arithmetic and logic unit for nanoelectronics. The main objective of this investigation is to design an ultra-low power and ultra-high-speed ALU design with QCA technology. The following QCA method has been implemented through reversible logic.Design/methodology/approachQCA logic is the main and critical condition for realizing NANO-scale design that delivers considerably fast integrate module, effective performable computation and is less energy efficiency at the nano-scale (QCA). Processors need an ALU in order to process and calculate data. Fault-resistant ALU in QCA technology utilizing reverse logic is the primary objective of this study. There are now two sections, i.e. reversible ALU (RAU), logical (LAU) and arithmetical (RAU).FindingsA reversible 2 × 1 multiplexer based on the Fredkin gate (FRG) was developed to allow users to choose between arithmetic and logical operations. QCA full adders are also implemented to improve arithmetic operations' performance. The ALU is built using reversible logic gates that are fault-tolerant.Originality/valueIn contrast to earlier research, the suggested reversible multilayered ALU with reversible QCA operation is imported. The 8- and 16-bit ALU, as well as logical unit functioning, is designed through fewer gates, constant inputs and outputs. This implementation is designed on the Mentor Graphics QCA tool and verifies all functionalities.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116519629","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}

{"title":"Pythagorean fuzzy prioritized aggregation operators with priority degrees for multi-criteria decision-making","authors":"H. Farid, Muhammad Riaz","doi":"10.1108/ijicc-10-2021-0224","DOIUrl":"https://doi.org/10.1108/ijicc-10-2021-0224","url":null,"abstract":"PurposeThe authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.Design/methodology/approachIn real-world situations, Pythagorean fuzzy numbers are exceptionally useful for representing ambiguous data. The authors look at multi-criteria decision-making issues in which the parameters have a prioritization relationship. The idea of a priority degree is introduced. The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.FindingsThe authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.Originality/valueThe aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128223952","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}

{"title":"Robust multifocus deep neural network for progression prediction on patient trajectory data","authors":"K. Arunkumar, S. Vasundra","doi":"10.1108/ijicc-09-2021-0202","DOIUrl":"https://doi.org/10.1108/ijicc-09-2021-0202","url":null,"abstract":"PurposePatient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research. In order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing methodologies. Hence deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation cost.Design/methodology/approachMultifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the architecture. Classifier is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new variant. The performance of disease progression learning progress utilizes the precision of the constituent classifiers, which usually has larger generalization benefits than those optimized classifiers.FindingsDeep learning architecture uses weight function, bias function on input layers and max pooling. Outcome of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease, and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected network. Experimental results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and accuracy.Originality/valueProposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient trajectory. Then, the generative output layer of the proposed model produces the progression outcome of the disease of the particular patient trajectory. The model tries to produce the accurate prognosis outcomes by employing data conditional probability function. The originality of the work defines 70% and comparisons of the previous methods the method of values are accurate and increased analysis of the predictions.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127105993","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}

{"title":"A multi-preference integrated algorithm (MPIA) for the deep learning-based recommender framework (DLRF)","authors":"Vikram Maditham, N. Reddy, Madhavi Kasa","doi":"10.1108/ijicc-11-2021-0257","DOIUrl":"https://doi.org/10.1108/ijicc-11-2021-0257","url":null,"abstract":"PurposeThe deep learning-based recommender framework (DLRF) is based on an improved long short-term memory (LSTM) structure with additional controllers; thus, it considers contextual information for state transition. It also handles irregularities in the data to enhance performance in generating recommendations while modelling short-term preferences. An algorithm named a multi-preference integrated algorithm (MPIA) is proposed to have dynamic integration of both kinds of user preferences aforementioned. Extensive experiments are made using Amazon benchmark datasets, and the results are compared with many existing recommender systems (RSs).Design/methodology/approachRSs produce quality information filtering to the users based on their preferences. In the contemporary era, online RSs-based collaborative filtering (CF) techniques are widely used to model long-term preferences of users. With deep learning models, such as recurrent neural networks (RNNs), it became viable to model short-term preferences of users. In the existing RSs, there is a lack of dynamic integration of both long- and short-term preferences. In this paper, the authors proposed a DLRF for improving the state of the art in modelling short-term preferences and generating recommendations as well.FindingsThe results of the empirical study revealed that the MPIA outperforms existing algorithms in terms of performance measured using metrics such as area under the curve (AUC) and F1-score. The percentage of improvement in terms AUC is observed as 1.3, 2.8, 3 and 1.9% and in terms of F-1 score 0.98, 2.91, 2 and 2.01% on the datasets.Originality/valueThe algorithm uses attention-based approaches to integrate the preferences by incorporating contextual information.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125153411","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}