VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE最新文献

{"title":"Sketch Based Image Retrieval using Deep Learning Based Machine Learning","authors":"Deepika Sivasankaran, S. P, R. R, M. Kanmani","doi":"10.35940/ijeat.e2622.0610521","DOIUrl":"https://doi.org/10.35940/ijeat.e2622.0610521","url":null,"abstract":"Sketch based image retrieval (SBIR) is a sub-domain\u0000of Content Based Image Retrieval(CBIR) where the user provides\u0000a drawing as an input to obtain i.e retrieve images relevant to the\u0000drawing given. The main challenge in SBIR is the subjectivity of\u0000the drawings drawn by the user as it entirely relies on the user's\u0000ability to express information in hand-drawn form. Since many\u0000of the SBIR models created aim at using singular input sketch\u0000and retrieving photos based on the given single sketch input, our\u0000project aims to enable detection and extraction of multiple\u0000sketches given together as a single input sketch image. The\u0000features are extracted from individual sketches obtained using\u0000deep learning architectures such as VGG16 , and classified to its\u0000type based on supervised machine learning using Support Vector\u0000Machines. Based on the class obtained, photos are retrieved from\u0000the database using an opencv library, CVLib , which finds the\u0000objects present in a photo image. From the number of\u0000components obtained in each photo, a ranking function is\u0000performed to rank the retrieved photos, which are then displayed\u0000to the user starting from the highest order of ranking up to the\u0000least. The system consisting of VGG16 and SVM provides 89%\u0000accuracy.","PeriodicalId":23601,"journal":{"name":"VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88845724","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":"Outlier Detection in High Dimensional Data","authors":"A. L, N. G S","doi":"10.35940/ijeat.e2675.0610521","DOIUrl":"https://doi.org/10.35940/ijeat.e2675.0610521","url":null,"abstract":"Artificial intelligence (AI) is the science that allows\u0000computers to replicate human intelligence in areas such as\u0000decision-making, text processing, visual perception. Artificial\u0000Intelligence is the broader field that contains several subfields\u0000such as machine learning, robotics, and computer vision.\u0000Machine Learning is a branch of Artificial Intelligence that\u0000allows a machine to learn and improve at a task over time. Deep\u0000Learning is a subset of machine learning that makes use of deep\u0000artificial neural networks for training. The paper proposed on\u0000outlier detection for multivariate high dimensional data for\u0000Autoencoder unsupervised model.","PeriodicalId":23601,"journal":{"name":"VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83709341","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 Assessment of Public Transport Accessibility Levels for Slums in Bhopal","authors":"Aditya Saxena, Vallary Gupta, B. Shrivastava","doi":"10.35940/ijeat.e2786.0610521","DOIUrl":"https://doi.org/10.35940/ijeat.e2786.0610521","url":null,"abstract":"Good connectivity and accessibility ensure\u0000inclusivity of public transport system which is an indicator of a\u0000fair society. The modal shift of commuters towards public transit\u0000services depends majorly on its accessibility levels. To ensure that\u0000commuters have equal opportunities to access jobs, education, and\u0000other services, PTAL (public transit accessibility level) is often\u0000evaluated. Public transport accessibility levels are a detailed and\u0000an accurate measure of accessibility of a point to the public\u0000transport network which considers walk access time and service\u0000availability. Public transportation is often referred to as an\u0000affordable model for every section of society due to its cheap fare\u0000price. The major question lies in the inclusivity of public transit\u0000services for the economically weaker section of society whether or\u0000not public transportation is available and accessible for those who\u0000need it or those who cannot afford other mobility services. The\u0000present study intends to focus on affordable and inclusive\u0000transportation for economically weaker sections. The study is an\u0000attempt to assess the issues with public transport services in the\u0000city of Bhopal, India for economically backward areas like slums.\u0000The research will help in understanding the accessibility level of\u0000currently available public transit services by evaluating the PTAL\u0000(public transit accessibility level) for socially backward.","PeriodicalId":23601,"journal":{"name":"VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78205398","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":"Emergence of Futuristic HRM in Perspective of Human - Cobot’s Collaborative Functionality","authors":"Anika Saxena, Abhay Saxena, Rajeev Sharma, M. Parashar","doi":"10.35940/ijeat.e2763.0610521","DOIUrl":"https://doi.org/10.35940/ijeat.e2763.0610521","url":null,"abstract":"Industry 4.0 buzzed out with a theme of “Smart\u0000Manufacturing for the Future”. With the advent of Industry 5.0,\u0000the world of technology is registering a paradigm shift from\u0000Customization to Personalization. Advanced Manufacturing,\u0000Cognitive Computing, AI, Robotics, Collaborative Robots, where\u0000all these technologies altogether introduces Industry 5.0 onto the\u0000stage. Cobot’s will be part of Human Resource Management. This\u0000Paper aims to visualize the Futuristic HRM and to understand the\u0000Collaborative Robots performance with Human HR’s. In this\u0000study, we had discussed the possible issues related to human-robot\u0000collective functionality from the organizational and HRM\u0000perspective. We had also suggested the effective role of HR and\u0000Cobot HR while dealing with the human and machine employees.\u0000We believe that the issues identified in this study will pave way for\u0000many upcoming organizational robotics research studies.","PeriodicalId":23601,"journal":{"name":"VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81634925","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":"Moss as Bio-indicator for Air Quality Monitoring at Different Air Quality Environment","authors":"N. Yatim, Nur Izzatul Afifah Azman","doi":"10.35940/ijeat.e2579.0610521","DOIUrl":"https://doi.org/10.35940/ijeat.e2579.0610521","url":null,"abstract":"Air quality monitoring by using bio-indicator\u0000currently being promoted and frequently used in studies due to\u0000their advantages compared to other scientific approaches. The\u0000advantages of using bio-indicator as a bio-monitoring in air\u0000quality are, it remains the cheapest, most available and simplest\u0000matrix for reliable atmospheric monitoring. This study was\u0000conducted to determine moss ability to be used as a bio-indicator\u0000for air quality monitoring when expose to different air quality\u0000environments. Four environmental conditions were chosen to\u0000conduct this study; urban area, reserve forest, living room and\u0000smoker’s room. Leucobryum glaucum or Holland moss is used as\u0000the bio-indicator to monitor the air quality. Gridded containers of\u0000moss were left at each study location for the duration of two\u0000weeks. Physical observation was monitored weekly by examining\u0000colour changes of the moss. Survivability rate of the moss was\u0000determined by counting the numbers of grid where moss growth in\u0000each container. The data was recorded through physical\u0000observation of moss responses and survivability rate towards\u0000different air quality environment. The data was analyzed by using\u0000SPSS. Moss reacted accordingly towards different air quality\u0000environments. Moss reacts mostly at highly polluted environment,\u0000in smoker’s room by changing from fresh green to brownish in\u0000color. In conclusion, moss can be used as a bio-indicator in air\u0000quality monitoring to determine air quality condition because\u0000moss changes its physical appearance and growth rate by the\u0000influenced of surrounding environment.","PeriodicalId":23601,"journal":{"name":"VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88591911","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":"Sentiment Analysis using Deep Belief Network for User Rating Classification","authors":"Ravi Chandra, Basavaraj Vaddatti","doi":"10.35940/ijitee.h9233.0610821","DOIUrl":"https://doi.org/10.35940/ijitee.h9233.0610821","url":null,"abstract":"People’s attitudes, opinions, feelings and sentiments which are usually expressed in the written languages are studied by using a well known concept called the sentiment analysis. The emotions are expressed at various different levels like document, sentence and phrase level are studied by using the sentiment analysis approach. The sentiment analysis combined with the Deep learning methodologies achieves the greater classification in a larger dataset. The proposed approach and methods are Sentiment Analysis and deep belief networks, these are used to process the user reviews and to give rise to a possible classification for recommendations system for the user. The user assessment classification can be progressed by applying noise reduction or pre-processing to the system dataset. Further by the input nodes the system uses an exploration of user’s sentiments to build a feature vector. Finally, the data learning is achieved for the suggestions; by using deep belief network. The prototypical achieves superior precision and accuracy when compared with the LSTM and SVM algorithms.","PeriodicalId":23601,"journal":{"name":"VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91347715","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":"Development of a Notification System for Sending Assignment using Internet of Things\u0000Technology","authors":"N. Songneam","doi":"10.35940/ijeat.e2809.0610521","DOIUrl":"https://doi.org/10.35940/ijeat.e2809.0610521","url":null,"abstract":"The problem with submitting assignments to teachers\u0000is students submit the assignments to teacher, there are a lot of\u0000assignments submission per day to teacher, but sometime students\u0000cannot hand in their assignment to teacher because teacher teach\u0000other section, so teacher cannot acknowledge student submission.\u0000According to the problem, researcher has been working on this\u0000research. The purposes of the research were to: 1) design the\u0000notification system for sending the assignment using Internet of\u0000Things technology, 2) develop devices for submitting the\u0000assignment and notifying submission information via LINE\u0000application using Internet of Things technology, and 3) evaluate\u0000user satisfaction in using the notification system for sending the\u0000assignment using Internet of Things technology. The population\u0000is students and teachers in Phranakhon Rajabhat University. The\u0000sample group consisted of 50 people using the purposive sampling.\u0000The research instruments were: 1) the notification system for\u0000sending the assignment using Internet of Things technology, and\u00002) the assessment form for user satisfaction in using the\u0000notification system for sending the assignment using Internet of\u0000Things technology. The results of the research showed that the\u0000system functions are working properly. The evaluation consisted\u0000of four parts: 1) the system designing had good level (\u0000x\u0000=4.12)\u0000and the standard deviation was 0.48, 2) the system usability had\u0000very good level (\u0000x\u0000=4.52) and the standard deviation was 0.22, 3)\u0000the system benefit had very good level (\u0000x\u0000=4.68) and the standard\u0000deviation was 0.38, and 4) the system overview had good level\u0000(\u0000x\u0000=4.38) and the standard deviation was 0.56. The results of four\u0000parts from the evaluation showed that the system was effective.","PeriodicalId":23601,"journal":{"name":"VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76325991","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":"Crop Discrimination using Non-Imaging Hyperspectral Data","authors":"P. V. Janse, R. Deshmukh","doi":"10.35940/ijeat.e2802.0610521","DOIUrl":"https://doi.org/10.35940/ijeat.e2802.0610521","url":null,"abstract":"Crop type discrimination is still very challenging\u0000task for researchers using non-imaging hyperspectral data. It is\u0000because of spectral reflectance similarity between crops. In this\u0000research work we have discriminated between four crops wheat,\u0000jowar, bajara and maize. We have tried to overcome the problems\u0000which have been faced my researchers. Initially by visual\u0000analysis we have selected 22 reflectance band which shows the\u0000absorption property of particular molecules and classification\u0000technique is applied, but it has given us very poor result of\u0000classification. We observed only 24% classification accuracy. So\u0000we considered nine vegetation indices along with spectral bands\u0000and achieved better classification accuracy. ASD FieldSpec 4\u0000Spectroradiometer device is used for capturing spectral\u0000reflectance data. We calculated nine different vegetation indices\u0000and some selective reflectance bands are used for crop\u0000classification. We have used Support Vector Machine (SVM) for\u0000classification.","PeriodicalId":23601,"journal":{"name":"VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84301071","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":"Security Design Pattern for Login System through Authentication using Modified Sha-384\u0000Algorithm","authors":"Y. C. Kulkarni, S. D. J. Prof.","doi":"10.35940/ijitee.h9244.0610821","DOIUrl":"https://doi.org/10.35940/ijitee.h9244.0610821","url":null,"abstract":"The research has been carried out to develop secure login system by authenticating the login using modified SHA-384 algorithm. It derives 896-bit hash value for the password entered by the user in the user registration form and saves the credentials entered by the user in system’s database. Results obtained are evaluated by resolving the general attacks confirmed that the modified SHA-384 algorithm was more secured compared to the original SHA-384 algorithm as it was not broken using generic attacks such as brute force, rainbow table and other cracking tools available online such as Cain and Abel. The performance of the modified algorithm was measured with only 2 ms additional execution time from SHA-384.","PeriodicalId":23601,"journal":{"name":"VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88355424","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":"Modeling and Designed of a Monopole Antenna that Operate at 3.3 [GHz] for Future 5G sub 6 [GHz]","authors":"Gholam D Aghashirin, Maged Kafafy, Hoda S. Abdel-Aty Zohdy, M. Zohdy, Adam Timmons","doi":"10.35940/ijeat.e2832.0610521","DOIUrl":"https://doi.org/10.35940/ijeat.e2832.0610521","url":null,"abstract":"Antenna unit is an important part of ADAS L2, L2+\u0000and Automated Driving L3 systems. It needs to function as\u0000needed in dGPS, HD Map Correction Services, OEM Radios and\u0000Navigation Systems. The presented monopole antenna model for\u00005G below 6 [GHz] operating at 3.3 [GHz] is developed. This work\u0000demonstrates the modeling, design, and determining of monopole\u0000antenna with intended targeted applications within the\u0000automotive system emerging autonomous vehicles space and as\u0000well as 5G Wireless Cellular Technology domain. FEKO\u0000simulation is undertaken rather than mathematical modeling to\u0000create the structure and conduct the analysis of the proposed\u0000monopole antenna. In order to support the fifth generation (5G)\u0000of wireless communication networks, SOS messages, vehicle\u0000tracking, remote vehicle start, Advanced Driver Assistance\u0000Systems (ADAS) L2, L2+/ Autonomous Driving (AD) L3 systems\u0000self-driving vehicles powered by 5G with rapidly growing sets of\u0000ADAS and AD features and functions within the autonomous\u0000space, USA cellular carriers mobile phone communication\u0000standard 4G MISO and 5G MIMO, LTE1, LTE2, connected\u0000functions, features/services, IoT, DSRC, V2X, and C-V2X\u0000applications and 5G enable vehicles destined for the NAFTA\u0000(USA, Canada and Mexico) market, a new single monopole\u0000antenna that operate at 3.3 [GHz] for future 5G (MIMO) below 6\u0000[GHz] modeling, design and simulation with intended automotive\u0000applicability and applications is proposed.\u0000The presented novel new 5G below 6 [GHz] monopole\u0000antenna:\u00001. Is not being investigated on the literatures review and\u0000published papers studied.\u00002. No paper exists on these frequency bands.\u00003. The desired monopole antenna is a new antenna with fewer\u0000components, reduction in size, low profile, competitive cost,\u0000better response to received RF signals for frequencies for\u0000future 5G below 6 [GHz] with each of the following:\u0000a. Range of operating frequencies, 0.6 [GHz] to\u00005.9256 [GHz].\u0000b. Center frequency = 3.2628 [GHz] ~ 3.3 [GHz] for\u0000the above band.\u0000c. Lambda (λ) = (3.0 x10^8 [m/sec^2]) / (3.3x10^9\u0000[Hz]) = 0.090 [m] = 90 [mm], lambda (λ) /4 = (0.090\u0000[m])/4 = 0.0225 [m] = 22.5 mm ~ 23 [mm], the overall\u0000monopole antenna height.\u0000To be more direct, simulation studies are carried out and are\u0000done utilizing FEKO software package from Altair to model the\u0000proposed monopole antenna for 5G below 6 [GHz] frequency\u0000band. The focus is on the frequency band for 5G sub 6 [GHz]\u0000cellular system.\u0000The paper will introduce the following key points:\u00001. Modelled and anayzed single element 5G sub 6 [GHz]\u0000monopole antenna.\u00002. Student version of CAD FEKO program was used to design\u0000our desired monopole antenna with a wire feed excitation\u0000coupled with step-by-step instructions is undertaken to\u0000highlight the model geometry creation of our monopole\u0000antenna. POST FEKO program is used to plot and view our\u0000simulation results.\u00003. We report the development of 5G below 6 [GHz] for fifth\u0000generation (5G) system that meets automotive and vehicle\u0000homologation s","PeriodicalId":23601,"journal":{"name":"VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE","volume":"115 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80843794","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}