S. R, Issac Shelton J, Neeharika Challa, Saravanan G, S. V, S. R, Uvashree D
{"title":"Friendly Course Equalizing Cloud Service using Education Cloud","authors":"S. R, Issac Shelton J, Neeharika Challa, Saravanan G, S. V, S. R, Uvashree D","doi":"10.1109/iCCECE52344.2021.9534845","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534845","url":null,"abstract":"This research aims at creating course equalization in a single cloud education portal. The existing course equalization process is done manually, which is not an efficient way and makes the task complicated. The prevailing technique is less accurate and a systematized storage is not available, as data are recorded and preserved by staffs of equalizing committee. Whereas, in this course equalizer software provides portal for information storage and retrieval whenever required. Being an education cloud storage its records can be easily accessed. In this pandemic situation, Education is made online. So difficulty arises in doing the transferring process manually for the students. So developing a course equalizer software provides a higher satisfaction for the transferring students and university staff. Students are enabled to know about their existing credits transfer and credits equivalencies, while transferring to other institutions. This cloud portal provides accurate course equalization service anytime and anywhere access. Similarly, the credit transfer for each course is done by examining the credit points given by the university. Credit transferring is a crucial task and it has many difficulties. The proposed work is done based on the algorithm Course Equalizer Based on HAIC search. Credits transfer plays a vital role in course waivers, where the students need not repeat the same courses completed at their previous institution. Courses may not always be the same at all institutions, thereby examining the core of courses becomes mandatory, which is difficult to get it done manually. Thus all these chaos can be resolved by upgrading us with this course equalizer software.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132389343","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":"Risk Assessment of Forests Probed Using UAV Integrated Computing","authors":"Gent Imeraj, Juliana Hoxha, Maaruf Ali","doi":"10.1109/iCCECE52344.2021.9534851","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534851","url":null,"abstract":"A rural mountainous area, sparsely populated and unserved by the communications network and the electricity grid, but popular with tourists, is assessed for risk factors. The paper presents research undertaken for the Northern Albanian Alps, but applicable to any such similar areas, for the application of technology to assess, predict and prevent disasters whether natural or artificial. A networked algorithmic behavioural approach to detect relevant disaster management actors is presented with the objective to provide a risk assessment of the North Albanian forested regions. Disaster Management cells are proposed to be set-up at the valley level to address firstly forest fires through UAV (Unmanned Aerial Vehicles) integrated computing by using onboard sensors and the perspective of enlarging its scope of managing larger areas. The problem identified is how to make the most efficient spatial aerial coverage out of the circular surface acquired from the UAV sensors. The method adopted is biomimicry (or biomimetics), by imitating the hexagonal shape of honeycombs applied to the sensor data pattern. As a result of converting the circular dataset into a hexagonal one, a space with 83 percent effective data was able to be measured. Thus, higher efficiency and no redundant data is taken into consideration, reducing the processing speed of the system. Speed is crucial in timely risk assessment.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131762356","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}
Anwar Ali Sathio, Mazhar Ali Dootio, A. Lakhan, M. Rehman, Ali Orangzeb Pnhwar, M. A. Sahito
{"title":"Pervasive Futuristic Healthcare and Blockchain enabled Digital Identities-Challenges and Future Intensions","authors":"Anwar Ali Sathio, Mazhar Ali Dootio, A. Lakhan, M. Rehman, Ali Orangzeb Pnhwar, M. A. Sahito","doi":"10.1109/iCCECE52344.2021.9534846","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534846","url":null,"abstract":"These days, the usage of healthcare applications in the secure and authenticate blockchain network has been growing progressively. The distributed healthcare applications can store and shared data with another node without any centralized authority by exploiting the blockchain technology. However, existing proof of work inside blockchain did not consider the anomaly detection in healthcare networks and mobility of the workload widely ignored in the literature studies. In this paper, the study investigates the travelling salesman problem with the scheduling, threshold, and anomaly detection constraints in distributed fog nodes. The fog nodes are local servers and implementing inside the hospital to facilitate the users from different healthcare services. The study devises the mobility scheduler anomaly detection (MSAD) schemes which consist of two phases, e.g., the initial assignment of healthcare workloads to optimal fog nodes and anomaly detection and validation in the healthcare blockchain network. Simulation results show that MSAD outperformed in terms of scheduling, threshold, anomaly detection in the healthcare blockchain network as compared to baseline studies.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131900922","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":"The Utilization of “Google Meet” and “Zoom Meetings” to Support the Lecturing Process during the Pandemic of COVID-19","authors":"B. Wiyono, Henny Indreswari, A. P. Putra","doi":"10.1109/iCCECE52344.2021.9534847","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534847","url":null,"abstract":"The process of teaching-learning at all education levels, including at universities has changed due to the COVID-19 pandemic. The lecturing process tends to be conducted online. Many application programs are utilized, including Zoom Meetings and Google Meet. The use of these two application systems brings great benefits, but on the other hand, it also caused problems are faced by the students. Therefore, this study aimed at exploring and comparing the utilization of these two applications, along with the problems they arose, and solution strategies for solving them in the lecturing process. This study employs survey research. A total of 82 students were taken as a sample. A random sampling technique was used for taking them. In collecting data, questionnaires and documentation were used. To analyze the data used t-test, analysis of variance, descriptive statistical analysis, and qualitative analysis. The results showed that the students used Google Meet and Zoom Meetings a lot in the learning process. There are significant differences in the utilization of Google Meet and Zoom Meetings in the learning process. They use it a lot to take part in lecturing activities, doing assignments, conducting discussions, finding sources of the material, providing feedback, and carrying out learning and teaching evaluations. The most problems faced by the students are signal problems and internet quotas. The solution strategies used to solve the problems are increasing internet capacity, improving ICT mastery, communicating with lecturers and university leaders.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122939389","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":"Implementation of Dynamic Scanner to Protect the Documents from Ransomware using Machine Learning Algorithms","authors":"S. R, K. R, J. B.","doi":"10.1109/iCCECE52344.2021.9534855","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534855","url":null,"abstract":"Now-a-days malware analysis and detection is the most needed tool in today’s world. The malware attack is rapidly increasing in all areas especially in corporate sectors. Though there are plenty of tools were available to detect the malwares, the best solution will be the one which uses the machine learning algorithms. By using this, the model can be trained with different algorithm. Each algorithm produces different accuracy rate. From the experimentation, it is found that Random Forest algorithm is chosen as the best algorithm. The datasets that were fed to the model contains different features such as MD5, DLL Characteristics, Size Of Code etc. With this sample data, the model gets trained with the algorithm that has the best accuracy rate. The trained machine learning model is then saved for later use by the most script. The key achievements of this proposed work is to find a solution to detect the malwares before it affects the system by using the best techniques and by giving the high accuracy rate.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124426033","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}