Cybernetics and Information Technologies最新文献_第9页

A Proposal for Honeyword Generation via Meerkat Clan Algorithm 一种基于Meerkat Clan算法的蜜语生成方案

IF 1.2

Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI: 10.2478/cait-2022-0003

Yasser A. Yasser, A. Sadiq, Wasim Alhamdani

引用次数: 0

Data Fusion and the Impact of Group Mobility on Load Distribution on MRHOF and OF0 数据融合及群迁移对MRHOF和OF0载荷分布的影响

IF 1.2

Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI: 10.2478/cait-2022-0005

Raad S. Al-Qassas, Malik Qasaimeh

引用次数: 0

Early Student-at-Risk Detection by Current Learning Performance and Learning Behavior Indicators 通过当前学习表现和学习行为指标检测早期学生的风险

IF 1.2

Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI: 10.2478/cait-2022-0008

T. A. Kustitskaya, A. A. Kytmanov, M. Noskov

引用次数: 6

Combination of Resnet and Spatial Pyramid Pooling for Musical Instrument Identification 结合Resnet和空间金字塔池进行乐器识别

IF 1.2

Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI: 10.2478/cait-2022-0007

Christine Dewi, Rung-Ching Chen

引用次数: 7

Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis 新型冠状病毒危机中网络钓鱼攻击检测的增强分析方法

IF 1.2

Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI: 10.2478/cait-2022-0004

Mousa Tayseer Jafar, Mohammad Al-Fawa'reh, Malek Barhoush, Mohammad H. Alshira'H

引用次数: 2

Blockchain-Enabled Supply-Chain in Crop Production Framework 作物生产框架中的区块链支持供应链

IF 1.2

Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI: 10.2478/cait-2022-0010

I. Radeva, I. Popchev

引用次数: 10

ESAR, An Expert Shoplifting Activity Recognition System ESAR，一个专业的商店行窃活动识别系统

IF 1.2

Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI: 10.2478/cait-2022-0012

Mohd. Aquib Ansari, D. Singh

{"title":"ESAR, An Expert Shoplifting Activity Recognition System","authors":"Mohd. Aquib Ansari, D. Singh","doi":"10.2478/cait-2022-0012","DOIUrl":"https://doi.org/10.2478/cait-2022-0012","url":null,"abstract":"Abstract Shoplifting is a troubling and pervasive aspect of consumers, causing great losses to retailers. It is the theft of goods from the stores/shops, usually by hiding the store item either in the pocket or in carrier bag and leaving without any payment. Revenue loss is the most direct financial effect of shoplifting. Therefore, this article introduces an Expert Shoplifting Activity Recognition (ESAR) system to reduce shoplifting incidents in stores/shops. The system being proposed seamlessly examines each frame in video footage and alerts security personnel when shoplifting occurs. It uses dual-stream convolutional neural network to extract appearance and salient motion features in the video sequences. Here, optical flow and gradient components are used to extract salient motion features related to shoplifting movement in the video sequence. Long Short Term Memory (LSTM) based deep learner is modeled to learn the extracted features in the time domain for distinguishing person actions (i.e., normal and shoplifting). Analyzing the model behavior for diverse modeling environments is an added contribution of this paper. A synthesized shoplifting dataset is used here for experimentations. The experimental outcomes show that the proposed approach attains better consequences up to 90.26% detection accuracy compared to the other prevalent approaches.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":"22 1","pages":"190 - 200"},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47463371","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: 9

Hy-MOM: Hybrid Recommender System Framework Using Memory-Based and Model-Based Collaborative Filtering Framework Hy-MOM:基于记忆和模型协同过滤的混合推荐系统框架

IF 1.2

Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI: 10.2478/cait-2022-0009

G. George, Anisha M. Lal

引用次数: 3

Deterministic Centroid Localization for Improving Energy Efficiency in Wireless Sensor Networks 提高无线传感器网络能量效率的确定质心定位

IF 1.2

Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI: 10.2478/cait-2022-0002

Sneha Vijayan, Nagarajan Munusamy

{"title":"Deterministic Centroid Localization for Improving Energy Efficiency in Wireless Sensor Networks","authors":"Sneha Vijayan, Nagarajan Munusamy","doi":"10.2478/cait-2022-0002","DOIUrl":"https://doi.org/10.2478/cait-2022-0002","url":null,"abstract":"Abstract Wireless sensor networks are an enthralling field of study with numerous applications. A Wireless Sensor Network (WSN) is used to monitor real-time scenarios such as weather, temperature, humidity, and military surveillance. A WSN is composed of several sensor nodes that are responsible for sensing, aggregating, and transmitting data in the system, in which it has been deployed. These sensors are powered by small batteries because they are small. Managing power consumption and extending network life is a common challenge in WSNs. Data transmission is a critical process in a WSN that consumes the majority of the network’s resources. Since the cluster heads in the network are in charge of data transmission, they require more energy. We need to know where these CHs are deployed in order to calculate how much energy they use. The deployment of a WSN can be either static or random. Although most researchers focus on random deployment, this paper applies the proposed Deterministic Centroid algorithm for static deployment. Based on the coverage of the deployment area, this algorithm places the sensors in a predetermined location. The simulation results show how this algorithm generates balanced clusters, improves coverage, and saves energy.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":"22 1","pages":"24 - 39"},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46006469","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: 2

Long Short Term Memory Neural Network-Based Model Construction and Fne-Tuning for Air Quality Parameters Prediction 基于长短期记忆神经网络的空气质量参数预测模型构建与Fne整定

IF 1.2

Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI: 10.2478/cait-2022-0011

Virendra Barot, V. Kapadia

{"title":"Long Short Term Memory Neural Network-Based Model Construction and Fne-Tuning for Air Quality Parameters Prediction","authors":"Virendra Barot, V. Kapadia","doi":"10.2478/cait-2022-0011","DOIUrl":"https://doi.org/10.2478/cait-2022-0011","url":null,"abstract":"Abstract Air pollution has increased worries regarding health and ecosystems. Precise prediction of air quality parameters can assist in the effective action of air pollution control and prevention. In this work, a deep learning framework is proposed to predict parameters such as fine particulate matter and carbon monoxide. Long Short Term Memory (LSTM) neural network-based model that processes sequences in forward and backward direction to consider the influence of timesteps in both directions is employed. For further learning, unidirectional layers’ stacking is implemented. The performance of the model is optimized by fine-tuning hyperparameters, regularization techniques for overfitting resolution, and various merging options for the bidirectional input layer. The proposed model achieves good optimization and performs better than the simple LSTM and a Recurrent Neural Network (RNN) based model. Moreover, an attention-based mechanism is adopted to focus on more significant timesteps for prediction. The self-attention approach improves performance further and works well especially for longer sequences and extended time horizons. Experiments are conducted using real-world data collected, and results are evaluated using the mean square error loss function.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":"22 1","pages":"171 - 189"},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44543293","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: 2