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For competitive hotel business, the hotel must have a sound prediction capability to balance the demand and supply of hospitality products. To have a sound prediction capability in the hotel, it should be prepared to be equipped with a new technology such as quantum computing. The quantum computing is a brand new cutting-edge technology. It will change hotel business and even the whole world too. Therefore, we study the impact of quantum computing on supply chain management (SCM) and hotel performance. Toward the goal we have developed the research model including six constructs: quantum (computing) prediction, communication, supplier relationship, service quality, non-financial performance, and financial performance. The result of the study shows a significant influence of quantum (computing) prediction on hotel performance through the mediating role of SCM in the hotel. Quantum prediction is highly significant in enhancing the SCM in the hotel. However, the direct effect between the quantum prediction and hotel performance is not significant. The finding indicates that hotels which would install the quantum computing technology and utilize the quantum prediction could hugely benefit from the performance improvement.

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.

Aim of this research was to investigate the effects of Digital Elevation Model (DEM) for sensitivity analysis with two types of DEMs: 1 to 24,000 and 1 to 250,000 DEM. Another emphasis was given to the development of methodology for processing DEMs to create ArcGIS Pro and GRASS layers. This was done while developing water quality system modeling using DEMs which were used to model hydrological processes and SWAT model. Sensitivity analysis with DEMs resulted in different runoff volumes in the model simulation. Runoff volume was higher for the 1:24,000 DEM than 1:250,000 DEM, probably due to the finer resolution and slope which increased the estimated runoff from the watershed. Certainly the DEMs were factors in precision of the simulations and it was obvious during sensitivity analysis that DEMs had significant effect on runoff volumes. We suggest, however, that additional comparative research could be conducted involving more parameters such as soil and hydrologic parameters to provide insight into the overall physical system which the SWAT model represents.

Considering the role of a financial analyst that directly affects investors as an information mediator, management's decision to disclose to maximize corporate value will have an important impact on investors as well. On the other hand, whether or not managers vary the level of disclosure depending on the corporate form will have great implications for policy authorities. However, there is no domestic research on the relationship between the corporate form and the quality of voluntary disclosure. Our study shows that the corporate form tends to deepen the negative relationship between the proprietary information cost and the quality of disclosure. Examining whether the relationship between proprietary information cost and management disclosure decision making is valid for domestic companies is expected to provide meaningful implications for investors and regulators. Depending on the corporate form, if an entity makes a discriminatory disclosure, the cost of capital will be affected. A more in-depth follow-up study on this should be done.

NAND flash memory-based SSD needs an internal software, Flash Translation Layer(FTL) to provide traditional block device interface to the host because of its physical constraints, such as erase-before-write and large erase block. However, because useful host-side information cannot be delivered to FTL through the narrow block device interface, SSDs suffer from a variety of problems such as increasing garbage collection overhead, large tail-latency, and unpredictable I/O latency. Otherwise, the new type of SSD, open-channel SSD exposes the internal structure of SSD to the host so that underlying NAND flash memory can be managed directly by the host-level FTL. Especially, I/O data classification by using host-side information can achieve the reduction of garbage collection overhead. In this paper, we propose a new scheme to reduce garbage collection overhead of open-channel SSD by separating the journal from other file data for the journaling filesystem. Because journal has different lifespan with other file data, the Write Amplification Factor (WAF) caused by garbage collection can be reduced. The proposed scheme is implemented by modifying the hostlevel FTL of Linux and evaluated with both Fio and Filebench. According to the experiment results, the proposed scheme improves I/O performance by 46%~50% while reducing the WAF of open-channel SSDs by more than 33% compared to the previous one.

This study aims to examine whether there are some differences in the cost stickiness of R&D expenses of IT companies before and after the COVID-19 crisis. Before COVID-19, IT companies did not reduce R&D expenses even if sales decreased, resulting in cost stickiness. As a result, it appears that during the COVID-19, IT companies adjust R&D expenses in proportion to changes in sales. This is interpreted as a reduction in R&D investment, which takes a long time to create results, in case of a decrease in sales due to uncertainty in future management performance due to the COVID-19 pandemic. In other words, during the COVID-19 risk period, IT companies, like other companies, reduced R&D expenses as sales decreased, resulting in reduced cost stickiness. This study differs from existing literature in that it examines the impact of the COVID-19 pandemic on the R&D expenditure behavior of companies in the IT industry.

As the number of mobile devices has been increasing tremendously, system capacity should be enlarged in future next generation communication, such as the fifth-generation (5G) and beyond 5G (B5G) mobile networks. For such future networks, non-orthogonal multiple access (NOMA) has been considered as promising multiple access technology. In this paper, to reduce both latency and complexity in existing NOMA, we propose non-successive interference cancellation (SIC) NOMA with asymmetric binary pulse amplitude modulation (2PAM), nearly without bit-error rate (BER) loss. First, we derive the closed form of BER expressions for non-SIC NOMA with asymmetric 2PAM, especially under Rayleigh fading channels. Then, it is shown that the BER performance of the stronger channel user who is supposed to perform SIC in conventional NOMA can be nearly achieved by the proposed non-SIC NOMA with asymmetric 2PAM, especially without SIC. Furthermore, we also show that the BER performance of the weaker channel user in conventional NOMA can be more closely achieved by the proposed non-SIC NOMA with asymmetric 2PAM. These BERs are shown to be achieved over the part of the power allocation range, which is consistent with the NOMA principle of user fairness. As a result, the non-SIC NOMA scheme with asymmetric 2PAM could be considered as a promising NOMA scheme toward next generation communication.

In this paper, we propose a system that allows small business owners focusing on the restaurant business to easily understand the management situation, and to manage the operation and management centering on the cost of food materials and profits and losses. In general, the metadata structure is different depending on the POS system, so it is necessary to first develop a standardized metadata model for a food material cost management system for small business owners in various industries. For that reason, the system proposed in this paper was applied to the cost management app by referring to the development of a data model using the metadata standard. In addition, in order to implement a cost profit/loss management system for small business owners in the restaurant industry, it was designed to support standardized metadata models from various types of POS systems, and is a hybrid app that can support a smart environment. Interface) was configured.

Blockchain is a technology that enables trust-based consensus and verification based on a decentralized network. Distributed ID (DID) is based on a decentralized structure, and users have the right to manage their own ID. Recently, interest in self-sovereign identity authentication is increasing. In this paper, as a method for transparent and safe sovereignty management of data, among data pseudonymization techniques for blockchain use, various methods for data encryption processing are examined. The public key technique (homomorphic encryption) has high flexibility and security because different algorithms are applied to the entire sentence for encryption and decryption. As a result, the computational efficiency decreases. The hash function method (MD5) can maintain flexibility and is higher than the security-related two-way encryption method, but there is a threat of collision. Zero-knowledge proof is based on public key encryption based on a mutual proof method, and complex formulas are applied to processes such as personal identification, key distribution, and digital signature. It requires consensus and verification process, so the operation efficiency is lowered to the level of O (logeN) ~ O(N2). In this paper, data encryption processing for blockchain DID, based on zero-knowledge proof, was proposed and a one-way encryption method considering data use range and frequency of use was proposed. Based on the content presented in the thesis, it is possible to process corrected zero-knowledge proof and to process data efficiently.

As pass the three revised bills, the Personal Information Protection Act was revised to have a larger application for personal information. For an industrial development through an efficient and secure usage of personal information, there is a need to revise the existing anonymity processing method. This paper modifies the Zero Knowledge Proofs algorithm among the anonymity processing methods to modify the anonymity process calculations by taking into account the reliability of the used service company. More detail, the formula of ZKP (Zero Knowledge Proof) used by ZK-SNAKE is used to modify the personal information for pseudonymization processing. The core function of the proposed algorithm is the addition of user variables and adjustment of the difficulty level according to the reliability of the data user organization and the scope of use. Through Setup_p, the additional variable γ can be selectively applied according to the reliability of the user institution, and the degree of agreement of Witness is adjusted according to the reliability of the institution entered through Prove_p. The difficulty of the verification process is adjusted by considering the reliability of the institution entered through Verify_p. SimProve, a simulator, also refers to the scope of use and the reliability of the input authority. With this suggestion, it is possible to increase reliability and security of anonymity processing and distribution of personal information.

This study proposes a method to reduce the phase error of the received signal to detect the object bearing. The phase shift of the received signal occurs due to the multipath of the signal by natural structure or artificial structures. When detecting the direction of the object using radio waves, the phase of the received signal cannot be accurately detected because of the phase bearing error in the object detection direction. The object detection direction estimation depends on the phase difference, antenna installation distance, signal source wavelength, frequency band and bearing angle. This study reduces the error of the phase bearing by using the phase delay of the relative phase difference for the signals incident on the two antennas. Through simulation, we analyzed the object direction detection performance of the proposed method and the existing method. Three targets are detected from the [−15°,0°,15°] direction. The existing method detects the target at [−13°,3°,17°], and the proposed method detects the at [−15°,0°,15°]. As a result of the simulation, the target detection direction of the proposed method is improved by 2 degrees compared to the existing method.

Purpose: It has been reported that the diversity and abundance of microbes in the vagina decrease due to the use of antimicrobial agents, and the high recurrence rate of female vaginitis due to this suggests that a new treatment is needed. Methods: In the experiment, we detected that 10% potassium sorbate solution, 1% eucalyptus oil solution, 1% tea tree oil solution, 400 μL/10 mL grapefruit seed extract solution, 100% lactic acid, 10% acetic acid solution, and 10% lactic acid solution were prepared and used. After adjusting the pH to 4, 5, and 6 with lactic acid and acetic acid in the mixed culture medium, each bacterium was inoculated into the medium and incubated for 72 h at 35°C. Incubate and 0 h each. 24 h. 48 h. The number of bacteria was measured after 72 h. Results: In the mixed culture test between lactic acid bacteria and pathogenic microorganisms, lactic acid bacteria showed good results at pH 5–5.5. Potassium sorbate, which has varying antibacterial activity based on the pH, killed pathogenic bacteria and allowed lactic acid bacteria to survive at pH 5.5. Conclusion: The formulation ratio obtained through this study could be used for the development of a feminine cleanser that can be used as a substitute for antibacterial agents. Further, the findings of this study may be able to solve the problem of antimicrobial resistance in the future.

In this research, we present a natural lighting system with transmission distance of 30m and lighting efficiency of 35% (30m standard) for operating hours of 7h/day (based on clear sky). The system is composed of parabolic reflective mirror and modified light pipe that can secure more than 88% of light concentration efficiency. The light loss rate of newly designed light pipe transmission system is demonstrated to 0.8 %/m in the straight-line part and 2%/m in the curved part. Modified light pipe daylighting system shows better performance over fiber optic daylighting system in terms of transmission distance (1.5 times longer) and illuminance (3.05 times higher).

Among transport vehicles, Special Vehicles (SVs) are seriously exposed to energy and environmental problems. In particular, elevator cars used when moving objects in high-rise buildings increase the engine's rotational speed (radian per second: RPM). At this time, when the vehicle accelerates rapidly while idling, energy consumption increases explosively along with the engine speed, and a lot of soot is generated. The purpose of this paper is to develop a bi-directional DC-DC converter for control of vehicle power and secondary battery used in an elevated ladder vehicle (EC) used in the moving industry. As a result of this paper, the performance test of the converter was conducted. The charging/discharging state of the converter was simulated using DC power supply and DC electronic load, and a performance experiment was conducted to measure the input/output power of the converter through a power meter. Through this experimental result, it was confirmed that the efficiency was more than 92% in Buck mode and Boost mode at maximum 1.2kW output.

In the Fourth Industrial Revolution and the foundation of software and hardware technologies through ICT, the technology to analyze the principles of information processing activities is the ability to implement programming. In this study, to improve the programming academic performance of electronics majors, firstly, we presented an effective teaching method in order to promote employment in the programming field by improving problem-solving skills and logical thinking skills in the programming field that electronics majors do not prefer. Secondly, we plan to promote intelligence informatization by converging intelligence information technology into the existing electronics industry by developing software utilization skills through programming curriculum that reflects the specificity and reality of electronics. Lastly, as computer programmers, we would like to expand creative talent education by developing learners' capabilities to cultivate smart talents who have both hardware and software capabilities.

The importance of mathematics is emerging to create new values and secure competitiveness in an intelligent information society based on the Fourth Industrial Revolution. This study was conducted with the aim of improving the academic performance and increasing interest of electronics majors in mathematics subjects. In order to develop learners' mathematical capabilities in major fields that utilize mathematics that electronics majors do not prefer, we have proposed a new teaching method to promote employment in mathematics-based electronics fields. In addition, to enhance learners’ self-directed learning, we developed teaching methods for efficient mathematics subjects with programming languages as tools in electronics engineering and applied them to real-world teaching sites to effectively cultivate academic performance improvement of majors. Finally, we conducted a survey and statistically analyze the effectiveness of the developed teaching methods to present effective operational measures for mathematics education, an essential tool in intelligent information technology.

The goal of this study was to explore the use of multiple SNS platforms and determine whether the number of used platforms affects one’s online self-presentations across the said platforms and if there is any difference in one’s online and offline self-presentations based on how many SNS platforms are used. This work studied online self-presentations, compared the on/offline ones and tried to find out if the inconsistencies of one’s own (observer’s) self-presentations both online (across platforms) and on/offline affected the observer’s impression formation (likability, trustworthiness and hypocrisy) of others. The study also aimed to find out if the impression of the others’ inconsistency both online and offline would differ based on the level of intimacy between the observer and the discussant. Three levels of intimacy were studied in order to do this: friends, acquaintances and strangers (online-only friends). The results showed that the more platforms people used the more inconsistent their online self-presentations got. Even though the results of the study showed barely significant relationship between the number of SNS accounts and one’s online and offline self-presentation, and partial connection between observer’s inconsistent self-presentations and impression formation of others, interestingly enough, the results managed to find significant differences between the impressions based on the level of intimacy between the observer and the discussants.

Railroad Systems, which are national infrastructure industries, cause unexpected property and human damage if they fail to function while operating. Accordingly, railroad facilities supporting the railroad system are areas where high reliability and safety are required. However, it is time for systematic and scientific maintenance to be taken away from the traditional maintenance methods, as the nation's railroad facilities are now aging seriously. The purpose of this study was to secure the safety and reliability of the aging railroad communication facilities and to improve their performance. The research subjects were selected as a precision diagnosis process for railroad communication facilities, and improvement points were derived through detailed precision diagnosis process analysis. It is deemed that this study can contribute based on securing stability, improving reliability, and continuous improvement of railroad communication facilities should be conducted in the operation of the entire railroad system.

With the recent rapid improvement in the standards of life and westernization of dietary lifestyles, the consumption of high-calorie diets such as high-fat and high-protein red meat and instant foods has increased, while less vegetables containing dietary fiber are consumed. In addition to that, stress, erroneous dietary behaviors, and contaminated environments are linked to the risk of developing ulcerative colitis, which is on the rise. Another cause of ulcerative colitis is that involve laxative abuse, including repeated, frequent use of laxatives, and include such conditions as deteriorated bowel function, irritable bowel syndrome, diarrhea, intestinal inflammation, etc. The present study aimed to investigate the comparative evaluation of pharmacological efficacy between sulfasalazine alone and combination with herbal medicine on dextran sodium sulfate (DSS)-induced UC in mice. Balb/c mice received 5% DSS in drinking water for 7 days to induce colitis. Animals were divided into five groups (n = 9): group I-normal group, group II-DSS control group, group III-DSS + sulfasalazine (30 mg/kg), group IV-DSS + sulfasalazine (60 mg/kg), group V-DSS + sulfasalazine (30 mg/kg) + Radish Extract mixture (30 mg /kg) (SRE). DSS-treated mice developed symptoms similar to those of human UC, such as severe bloody diarrhea and weight loss. SRE supplementation, as well as sulfasalazine, suppressed colonic length and mucosal inflammatory infiltration. In addition, SRE treatment significantly reduced the expression of pro-inflammatory signaling molecules through suppression both mitogen-activated protein kinases (MAPK) and nuclear factor-kappa B (NF-B) signaling pathways, and prevented the apoptosis of colon. Moreover, SRE administration significantly led to the up-regulation of antioxidant enzyme including SOD and Catalase. This is the first report that Radish extract mixture combined with sulfasalazine protects against experimental UC via the inhibition of both inflammation and apoptosis, very similar to the standard-of-care sulfasalazine.

In this paper, we propose an histogram weighted centroid based Siamese region proposal network for object tracking. The original Siamese region proposal network uses two identical artificial neural networks which take two different images as the inputs and decide whether the same object exist in both input images based on a similarity measure. However, as the Siamese network is pre-trained offline, it experiences many difficulties in the adaptation to various online environments. Therefore, in this paper we propose to incorporate the histogram weighted centroid feature into the Siamese network method to enhance the accuracy of the object tracking. The proposed method uses both the histogram information and the weighted centroid location of the top 10 color regions to decide which of the proposed region should become the next predicted object region.

The purpose of this study is to identify the impact of informatization minds of college students majoring in sports on information development efforts and academic achievement. Accordingly, a survey was conducted on 200 college students majoring in physical education at D University, and 197 students were selected as the final effective samples. Data processing was conducted using SPSS 23 for frequency analysis, reliability analysis, correlation analysis, and regression analysis. The results are as follows: First, recognition of the necessity of informatization education, a subfactor of the informatization mind, has a positive impact on informatino development efforts. Second, recognition of the necessity of informatization education, understanding and utilization of informatization concepts among sub-factors of informatization minds have a positive impact on the major learning achievement. Third, all the conversational factors in the informatization mind have a positive impact on study problem resolution. Fourth, information development efforts have a positive impact on the major learning achievement and the study problem resolution. Therefore, efforts are needed to increase the information mindset of college students major in sports.

In this paper, we find a suitable methodology for Korean Sentiment Analysis through a comparative experiment in which methods of embedding and natural network models are learned at the highest accuracy and fastest speed. The embedding method compares word embeddeding and Word2Vec. The model compares and experiments representative neural network models CNN, RNN, LSTM, GRU, Bi-LSTM and Bi-GRU with IKEA review data. Experiments show that Word2Vec and BiGRU had the highest accuracy and second fastest speed with 94.23% accuracy and 42.30 seconds speed. Word2Vec and GRU were found to have the third highest accuracy and fastest speed with 92.53% accuracy and 26.75 seconds speed.

We have been developed the smart tourism app and the Instagram and YouTube contents to provide personalized tourism information and travel product information to individual tourists. In this paper, we develop a rule-based smart tourism chatbot with the khaiii (Kakao Hangul Analyzer III) morphological analyzer and Neo4J graph database. In the proposed chatbot system, we use a morpheme analyzer, a proper noun dictionary including tourist destination names, and a general noun dictionary including containing frequently used words in tourist information search to understand the intention of the user's question. The tourism knowledge base built using the Neo4J graph database provides adequate answers to tourists' questions. In this paper, the nodes of Neo4J are Area based on tourist destination address, Contents with property of tourist information, and Service including service attribute data frequently used for search. A Neo4J query is created based on the result of analyzing the intention of a tourist's question with the property of nodes and relationships in Neo4J database. An answer to the question is made by searching in the tourism knowledge base. In this paper, we create the tourism knowledge base using more than 1300 Jeju tourism information used in the smart tourism app. We plan to develop a multilingual smart tour chatbot using the named entity recognition (NER), intention classification using conditional random field(CRF), and transfer learning using the pretrained language models.

This paper presents a margin-based face liveness detection method with behavioral confirmation to prevent spoofing attacks using deep learning techniques. The proposed method provides a possibility to prevent biometric person authentication systems from replay and printed spoofing attacks. For this work, a set of real face images and fake face images was collected and a face liveness detection model is trained on the constructed dataset. Traditional face liveness detection methods exploit the face image covering only the face regions of the human head image. However, outside of this region of interest (ROI) might include useful features such as phone edges and fingers. The proposed face liveness detection method was experimentally tested on the author’s own dataset. Collected databases are trained and experimental results show that the trained model distinguishes real face images and fake images correctly.

In recent years, with the rapid growth of China’s animation industry, the two-dimensional culture and consumption have been immersed in the daily lives of young people. The two-dimensional culture that mainly exists on young people is gradually entering the public’s field of vision, making the two-dimensional culture not It is only restricted to the fixed fan circle, but is known to more people outside the circle. At the same time, the "barrage" (screen text) cultures in video websites has become popular with some film and television works, Internet terms, etc., and has attracted the attention on mainstream culture. On the one hand, its cultural products have appeared on traditional mainstream video websites and advertisements on provincial satellite TV. And in the program, on the other hand, a small part of the screen text and cultural terms are also used by some celebrities and other ordinary people who don’t understand the meaning of the terms at all, and have caused widespread dissemination. Sometimes the video website itself is also mentioned, which obviously shows a difference. The tendency towards a kind of screen texts subculture to penetrate the mainstream culture.

3D-CNN is one of the deep learning techniques for learning time series data. However, these three-dimen-sional learning can generate many parameters, requiring high performance or having a significant impact on learning speed. We will use these 3D-CNNs to learn hand gesture and find the parameters that showed the highest accuracy, and then analyze how the accuracy of 3D-CNN varies through input data changes without any structural changes in 3D-CNN. First, choose the interval of the input data. This adjusts the ratio of the stop interval to the gesture interval. Secondly, the corresponding interframe mean value is obtained by meas-uring and normalizing the similarity of images through interclass 2D cross correlation analysis. This experi-ment demonstrates that changes in input data affect learning accuracy without structural changes in 3D-CNN. In this paper, we proposed two methods for changing input data. Experimental results show that input data can affect the accuracy of the model.

The five elements of big data are said to be Volume, Variety, Velocity, Veracity, and Value. Among them, data lacking the Veracity of the data or fake data not only makes an error in decision making, but also hinders the creation of value. This study analyzed YouTube's revenue structure to focus the effect of data integrity on data valuation among these five factors. YouTube is one of the OTT service platforms, and due to COVID-19 in 2020, YouTube creators have emerged as a new profession. Among the revenue-generating models provided by YouTube, the process of generating advertising revenue based on click-based playback was analyzed. And, analyzed the process of subtracting the profits generated from invalid activities that not the clicks due to viewers' pure interests, then paying the final revenue. The invalid activity in YouTube's revenue structure is Raw Data, not pure viewing activity of viewers, and it was confirmed a direct impact on revenue generation. Through the analysis of this process, the new Data Value Chain was proposed.

YouTube is an OTT service that leads the home economy, which has emerged from the 2020 Corona Pandemic. With the growth of OTT-based individual media, creators are required to establish attractive storytelling strategies that can be preferred by viewers and elected for YouTube recommendation algorithms. In this study, we conducted a study on modeling that proposes a content storyline for creators. As the ability for Creators to create content that viewers prefer, we have presented the data literacy ability to find patterns in complex and massive data. We also studied the importance of compelling storytelling configurations that viewers prefer and can be selected for YouTube recommendation algorithms. This study is of great significance in that it deviated from the viewer-oriented recommendation system method and proposed a story suggestion model for individual creaters. As a result of incorporating this story proposal model into the production of the YouTube channel Tiger Love video, it showed a certain effectiveness. This story suggestion model is a machine learning text-based story suggestion system, excluding the application of photography or video.

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model “A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction” to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

The economic value of personal information has its importance as an objective measure of valuation in commercial, legal, and policy areas. Until recently, however, personal information subjects have not properly recognized the economic value of personal information, which has led to the inability to exercise the right to self-determination of personal information by unconsciously agreeing to the terms and conditions of personal information service without recognizing the value of personal information provided to the service provider when subscribing to a specific service. Therefore, we will examine the methodologies for calculating the economic value of personal information and the practical guarantee of the right to self-determination of personal information and analyze the economic value of personal information through a survey. Also, we would like to propose various ways for the subject of personal information with limited cognitive resources to visually accept the economic value of personal information required by the terms and conditions and suggest the optimal visualization of personal information economic value to exercise the right to self-determination of personal information. To do so, in this paper, we have conducted two survey experiments to estimate the economic value of personal information. Based on the price of personal information by category retrieved from surveys, we have visualized the price of personal information in various forms and asked respondents to choose the optimal infographic that best represents the value of personal information visually. As a result, we have proposed an optimal usage of the infographic to ‘nudge’ information subjects about their right to self-determination of personal information, therefore opening the possibility of diminishing privacy paradox.