International Journal of Hybrid Information Technology最新文献

{"title":"The Study of Handwriting Recognition Algorithms Based on Neural Networks","authors":"Barak Finkelstein, Kaplan Kuncan","doi":"10.21742/IJHIT.2021.14.1.05","DOIUrl":"https://doi.org/10.21742/IJHIT.2021.14.1.05","url":null,"abstract":"Handwriting Identifies basic graph-like problems and has a high real-world value in areas such as cloud accounting, finance, and postal administration. Due to the unrestricted problem of handwritten numbers when writing, it is relatively difficult to achieve rapid and effective recognition. With the emergence of deep learning-related algorithms and the rapid development of computer hardware technology, image classification methods based on Convolutional Neural Network (CNN) have gradually become a research hotspot. Because the convolutional network has a strong letter numbering ability and network generalization ability, the recognition rate can often exceed the traditional graph classing method . Therefore, the study of hand-written word recognition should be implemented using CNN through the network. Handwriting Word Recognition is the key technique for self-identification. Therefore, summarizing and analyzing the existing handwritten digit recognition algorithms, two handwritten digit recognition algorithms based on Convolutional Neural Network (CNN) are proposed. To improve the recognition performance of the CNN model, this article proposes a handwriting recognition algorithm based on the change to CNN. To extract the image feature information more fully, this paper proposes a handwriting recognition algorithm based on feature fusion and SVM. First, using the modified CNN model and the Gabor filter that introduces curvature systems, extract the CNN and Gabor characteristics of the character image; Second, the characteristics of its progress are fused to obtain more effective new features; Finally, the fusion feature is entered into the SVM classifier into the line number of words to recognize. The results of the experiment show that the algorithm can effectively improve the recognition effect of handwritten words","PeriodicalId":170772,"journal":{"name":"International Journal of Hybrid Information Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114578706","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":"Systematic Analysis of Environmental Issues on Ecological Smart Bee Farm by Linear Regression Model","authors":"A. Rahman, Myeongbae Lee, Jonghyun Lim, Yongyun Cho, Changsun Shin","doi":"10.21742/IJHIT.2021.14.1.04","DOIUrl":"https://doi.org/10.21742/IJHIT.2021.14.1.04","url":null,"abstract":"Environmental food and nutritional protection primarily depend on pollination from bees. Historically, beekeeping has been performed in different locations as part of the local food community. Beekeeping is increasing rapidly these days due to the high demand for honey and farmers are taking various forms of beekeeping methods to achieve high yield. Honey production also depends on different types of environmental factors. The main principle of this study is to show the analysis results of various types of environmental factors for three different bee farms by the linear regression model to figure out the best farm among all three farms. To improve the production of honey, farmers have to consider different types of environmental factors and this is the elevated time to support farmers by technology. This study analyzed different types of environmental factors like farm outside temperature, farm inside temperature, farm humidity for three different smart bee farms by using a linear regression model to know about their environmental conditions. The performance of prediction models is measured by R 2 error, Root Mean Squared Error (RMSE), Standard Error values (SE), and Mean Absolute Error (MAE). Based on the outcome, it is observed that the best results giving farm is farm 3 that has been able to give R 2 value 0.95,0.95, and 0.72 for the farm outside temperature, inside temperature, and farm humidity.","PeriodicalId":170772,"journal":{"name":"International Journal of Hybrid Information Technology","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117140580","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":"Barter Exchange Economy: A New Solution Concept for Resource Sharing in Wireless Multimedia Cloud Networks","authors":"Rahman Mansoury, Mohammad Hossein Rezvani","doi":"10.21742/IJHIT.2021.14.1.01","DOIUrl":"https://doi.org/10.21742/IJHIT.2021.14.1.01","url":null,"abstract":"One of the most significant types of Mobile Cloud Networking (MCN) is Cloud-based Wireless Multimedia Social Networks (CWMSNs). We believe that microeconomics theory is a good candidate to model the bandwidth sharing operations in CWMSNs. We model the interactions of mobile users in terms of the barter exchange economy. In our modeling, bandwidth is chosen as the exchangeable commodity and mobile users and desktop users act as players . From a microeconomics point of view, the allocated bandwidth subject to each service plays the role of “endowment” (budget) for players. With this endowment and leveraging the concept of barter exchange, mobile users can interact with each other to gain more quality of service (QoS) in the future. We prove that by applying the exchange economy, users’ social welfare could reach to global maximum, known as Pareto efficiency. To the best of our knowledge, the idea of a barter exchange economy has never been employed in any study on cloud computing. Simulation results, obtained through the CloudSim framework, established the robustness of our modeling in terms of significant metrics such as social welfare, number of blocked users, satisfaction level, and Pareto efficiency.","PeriodicalId":170772,"journal":{"name":"International Journal of Hybrid Information Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121314937","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":"Improving Learning Performance in Neural Networks","authors":"F. Al-akashi","doi":"10.21742/IJHIT.2021.14.1.02","DOIUrl":"https://doi.org/10.21742/IJHIT.2021.14.1.02","url":null,"abstract":"In this paper, we propose an optimization framework for a robust deep learning algorithm using the influences of noisy recurring on artificial neural networks. Influences between nodes in the neural network remain very steady in the convergence towards a superior node even with several types of noises or rouges. Several characteristicss of noisy data sources have been used to optimize the observations in a group of neural networks during their learning process. While the standard network learns to emulate those around, it does not distinguish between professional and nonprofessional exemplars. A Collective system can accomplish and address such difficult tasks in both static and dynamic environments without using some external controls or central coordination. We will show how the algorithm approximates gradient descent of the expected solutions produced by the nodes in the space of pheromone trails. Positive feedback helps individual nodes to recognize and hone their skills, and covering their solution optimally and rapidly. Our experiment results showed how long-run disruption in the learning algorithm can successfully move towards the process that accomplishes favorable outcomes. Our results are comparable to and better than those proposed by other models considered significant, e.g., “large step Markov chain” and other local search heuristic algorithms.","PeriodicalId":170772,"journal":{"name":"International Journal of Hybrid Information Technology","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121502436","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":"Land Suitability Evaluation for Cassava Production Using Integral Value Ranked Fuzzy AHP and GIS Techniques","authors":"Atijosan Abimbola, E. Ewang, Badru Rahmon, A. Taofeek","doi":"10.21742/IJHIT.2021.14.1.03","DOIUrl":"https://doi.org/10.21742/IJHIT.2021.14.1.03","url":null,"abstract":"This study presents an improved integral value ranked Fuzzy Analytic Hierarchy Process (FAHP) and Geographic Information System (GIS) based Multi-Criteria Decision Making (MCDM) technique to help decision-makers/farmers evaluate and map suitable lands for optimum cassava production. Selected input/ suitability factors chosen from literature and experts’ opinion were: pH, organic carbon, cation exchange capacity, slope, aspect, elevation, temperature, relative humidity, rain, distance from river and road. The improved integral value ranked FAHP method was used in prioritizing and assigning weights to each causative factor in the MCDM process due to its effectiveness, consistency, and ease of implementation. Land suitability maps were created using GIS techniques based on the aggregation of the various input factors and their derived weights. The outcome of the aggregation was reclassified into four classes using the standard deviation classification method (this method shows how much a feature deviates from the mean). Results obtained showed that 40% of the total area was highly suitable (S1), 36% was moderate suitability (S2), 20% was marginally suitable (S3) and 4% was not suitable (N). Results also showed that pH and organic content of the soil were the major determinants of soil suitability for cassava cultivation in the study area. This study showed the effectiveness of the proposed approach in assessing and mapping suitable areas for optimum cassava production within the study area.","PeriodicalId":170772,"journal":{"name":"International Journal of Hybrid Information Technology","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125357901","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":"Deep Learning Approaches for Tomato Plant Disease Detection","authors":"Abdur Rahman","doi":"10.21742/IJHIT.2020.13.2.06","DOIUrl":"https://doi.org/10.21742/IJHIT.2020.13.2.06","url":null,"abstract":"Agriculture is the mainstream to keep pace in the Bangladeshi economy. Plant disease became a threat to food security as it is a very important factor to deteriorate the quality and quantity of harvest. Therefore, it is important to detect the plant diseases early which results in interrupting from falling the massive destruction of harvest. But, an erroneous diagnosis of the disease results in the inappropriate use of pesticides. In order to enhance the production quality and quantity, a deep learning-based approach is proposed to detect the tomato leaf diseases, and then classify the types of the disease using image dataset. This proposed approach trained two model architectures: inception V3 and Convolutional Neural Network (CNN). Inception V3 performs well and reaches a success rate with a 96.11% in order to identify whether the specific plant leaf is infected or healthy. The success rate is significant and makes this approach as a very useful way or early forewarning tool, and this approach might be an essential system to operate real agriculture fields. As the detection accuracy is recorded as 94.72% for CNN. We confirm that it achieves the experimental results with 94.72% and 96.11% for the detection and classification of infected leaves from dataset for CNN and Inception V3 respectively. 1","PeriodicalId":170772,"journal":{"name":"International Journal of Hybrid Information Technology","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115758927","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 Study on Proptech based Smart City and Smart Village Common Technology Demand","authors":"Jaehwan Kim, Yongkyung Cho","doi":"10.21742/IJHIT.2020.13.2.07","DOIUrl":"https://doi.org/10.21742/IJHIT.2020.13.2.07","url":null,"abstract":"Recently, proptech, which is a combination of property and technology, has been attracting attention, but it is necessary to apply proptech centered on urban spaces to areas centered on regions such as smart villages. Therefore, the purpose of this study was to derive common technology demands for smart cities for technology demand in urban areas and smart villages for rural technology demands based on the existing prop-tech concept for the real estate industry. As a method of research, we surveyed technology demand. In order to present a technology platform that can be reflected to prop technology from demand, it was set as a common technology demand range that encompasses this. This is because smart cities and smart villages are based on physical and technical environments in urban and nonurban areas, which means that the scope of the sharing economy and smart real estate technologies that reflect residential and convenient facilities are mutually reflected. Therefore, the common technology demand was divided into general and specialized types. The general type was categorized into cultural, welfare, and living environment services. The specialized type was divided into experience programs, visitor management, public relations, production distribution, accounting management, and facility management to derive detailed technical demands.","PeriodicalId":170772,"journal":{"name":"International Journal of Hybrid Information Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125651520","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":"Detection and Classification of Lung Nodule in Diagnostic CT: A TsDN method based on Improved 3D-Faster R-CNN and Multi-Scale Multi-Crop Convolutional Neural Network","authors":"M. B. Zia, Juan Zhao, X. Ning","doi":"10.21742/IJHIT.2020.13.2.04","DOIUrl":"https://doi.org/10.21742/IJHIT.2020.13.2.04","url":null,"abstract":"Lung nodule classification has been one of the major problem relevant to Computer-Aided Diagnosis (CAD) system. Lung cancer for both men and women has been one of the leading causes of cancer related death. Deep learning models have produced promising performance in recent years, outperforming traditional methods in different fields. Nowadays, scientists have attempted numerous deep learning approaches to enhance the efficiency of CAD systems via Computed Tomography (CT) in lung cancer screening. In this paper, we presented a completely automatic lung CT system for cancer diagnosis named Two-step Deep Network (TsDN) and it contains two parts detection of nodule and classification. First, Improved 3D-Faster R-CNN with U-net like encoder and decoder is used for detection of nodule and then Multi-scale Multi-crop Convolutional Neural Network (MsMc-CNN) is proposed for the pulmonary nodule classification. The multi scale approach uses filters of various sizes to extract nodule features more efficiently from the local regions, and then multi crop pooling technique involves in extracting the important nodule information that cultivates various regions from convolutional feature map and then add numerous times for the maximum pooling. The proposed TsDN is trained and evaluated on LIDC-IDRI public dataset and achieved a sensitivity of 0.885 and specificity of 0.922 with AUC of 0.946. U-Net-like encoder and decoder framework for the detection of lung nodule. The nodules found are then fed into classification part for the lung nodule classification. We use Multi-scale Multi-crop Convolutional Neural Network (MsMc-CNN) to extract features for classification. To know more efficiently about local structures, the suggested MsMc-CNN uses convolutional multi scale layers to obtain features at various scales, we also demonstrate that with the multi crop pooling approach, the trained deep features were capable of capturing nodule salient details. Finally, our model is fully trained to classify the lung nodule into benign and malignant. The experimental result on LUNA16 and LIDC-IDRI show the enhanced performance of proposed TsDN system.","PeriodicalId":170772,"journal":{"name":"International Journal of Hybrid Information Technology","volume":"1069 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116289536","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":"Monitoring and Detection of Security Events through IoT Device Identification Using Application Layer Protocols","authors":"Ammad Khan, Yongle Chen, Waqas Ahmad, Kamran Javed, M. B. Zia, Arooj Khan","doi":"10.21742/IJHIT.2020.13.2.01","DOIUrl":"https://doi.org/10.21742/IJHIT.2020.13.2.01","url":null,"abstract":"Internet of Things network is based on the distributed infrastructure as large of number of devices connected to the network makes the network an ultra-dense network. The profound devices are becoming capable of connecting to the other devices operating on different networks nature and different architecture thus giving birth to the heterogenic nature of the networks. In such environment where incident responders face challenges postured by the event occurred from IoT device networks becomes difficult to gather, analyze and examine its impending traces. This study proposed a contrivance to fetch and provide the information of the IoT devices connected to a certain network using protocols of application layers and associated open ports to the investigators and incident responders. This will be helpful in detecting and identifying the IoT devices connected to the network that will to a significant certainty aided to the work of investigators. For this purpose, a tool will be presented through series of experiments and algorithmic development. The results of the experiment shows that the proposed tool effectively identified the IoT devices associated with open ports and also classification of the IoT and non-IoT devices is achieved.","PeriodicalId":170772,"journal":{"name":"International Journal of Hybrid Information Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128983198","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":"Prega Care - A Health Monitoring Device for Pregnant Women","authors":"Khushboo Gairola, Neha Singh, R. Pant, A. Bagwari","doi":"10.21742/IJHIT.2020.13.2.05","DOIUrl":"https://doi.org/10.21742/IJHIT.2020.13.2.05","url":null,"abstract":"","PeriodicalId":170772,"journal":{"name":"International Journal of Hybrid Information Technology","volume":"102 4 Pt 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131403351","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}