2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)最新文献_第2页

Air Cushion Restraint and Extended Safety System with Sensor and Control Module 带有传感器和控制模块的气垫约束和扩展安全系统

2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) Pub Date : 2023-04-21 DOI: 10.1109/ICSTSN57873.2023.10151617

Kalpana Murugan, Kartheek Sunkara, Raghuvaran Reddy Yaramala, Sai Durga Pavan Kumar Parella, Durga Prasad Makkireddy

引用次数: 0

A Novel Replication-Less Image Retrieval Method from Cloud Platforms using Divergence Features 一种基于发散特征的云平台无复制图像检索方法

2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) Pub Date : 2023-04-21 DOI: 10.1109/ICSTSN57873.2023.10151628

S. Usharani, K. Dhanalakshmi

{"title":"A Novel Replication-Less Image Retrieval Method from Cloud Platforms using Divergence Features","authors":"S. Usharani, K. Dhanalakshmi","doi":"10.1109/ICSTSN57873.2023.10151628","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151628","url":null,"abstract":"Real-time multimedia applications provide visual and audible services collaborated with the cloud platform for heterogeneous user requirements. Multi-source information storage and fusion satisfy the user demands through the applications. Contrarily, replication is a common issue demanding high space and time, increasing computation and retrieval time. For addressing the retrieval issues in replicated image storage, this article introduces a Divergent Feature-induced Extension (DFIE) method for large cloud platforms. The proposed method identifies the divergences alone in the input features correlated with the stored ones, in different feature extracted instances. In the feature divergent analysis, the deep recurrent learning paradigm is utilized. The iterations are used for identifying non-correlating features and their deviation for verifying non-replicable images. The process pursues region-based segmentation for a feature and edge-based divergence and similarity identification. The proposed method’s performance is analyzed using the metrics detection accuracy, complexity, and computing time.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130004740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reinforcing IoT Security through Machine Learning Based Spam Detection 通过基于机器学习的垃圾邮件检测加强物联网安全

2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) Pub Date : 2023-04-21 DOI: 10.1109/ICSTSN57873.2023.10151544

R. Chatrapathi, M. Ramkumar, D. Jayakumar, R.Pooja Sri

{"title":"Reinforcing IoT Security through Machine Learning Based Spam Detection","authors":"R. Chatrapathi, M. Ramkumar, D. Jayakumar, R.Pooja Sri","doi":"10.1109/ICSTSN57873.2023.10151544","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151544","url":null,"abstract":"Millions of linked sensors and actuators make up the Internet of Things (IoT), a fast-growing network that transmits data over wired or wireless communication channels. IoT has grown significantly in the last ten years alone, with an estimated 43 billion connected devices predicted to be in use by 2023. Machine learning algorithms may be quite useful in this situation for establishing biotechnology-based security and authorization as well as for spotting abnormalities to improve the security and usability of IoT devices. Attackers frequently consider machine learning techniques to be possible weaknesses in sophisticated IoT devices, though. In order to secure IoT devices, the research suggests employing machine learning to identify spam. A machine-learning framework for spam detection in an IoT is the suggested method for achieving this goal. This framework assesses five alternative machine learning models using various measures and a wide range of input feature sets. A spam score is generated by each model using the improved input characteristics. Based on a number of variables, this score shows the degree of trustworthiness of an IoT device. The IoT-23 dataset is used to validate the proposed method. The findings show that the suggested solution is superior to the current approaches in terms of effectiveness.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133739125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Sensor Scheduling for Federated Learning over 6G in-X Subnetworks 基于6G in-X子网的联邦学习自适应传感器调度

2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) Pub Date : 2023-04-21 DOI: 10.1109/ICSTSN57873.2023.10151547

Qiaoli Wu, Bo Gao, Ke Xiong

引用次数: 0

Usability Evaluation of Flip Applications Using Cognitive Walkthrough and Think Aloud Evaluation Methods 使用认知演练和大声思考评估方法进行翻转应用程序的可用性评估

2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) Pub Date : 2023-04-21 DOI: 10.1109/ICSTSN57873.2023.10151607

Fannisha, M. L. Hamzah, Tengku Khairil Ahsyar, Eki Saputra

{"title":"Usability Evaluation of Flip Applications Using Cognitive Walkthrough and Think Aloud Evaluation Methods","authors":"Fannisha, M. L. Hamzah, Tengku Khairil Ahsyar, Eki Saputra","doi":"10.1109/ICSTSN57873.2023.10151607","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151607","url":null,"abstract":"The Flip application is a digital financial service that makes it easy for users to transfer between national and international banks without administrative fees, there are also digital wallet services such as purchasing credit, electricity, and others. Creating a good user experience, it takes an evaluation of the usability of the Flip application, to find out the level of user satisfaction and identify problems. Therefore, it is necessary to evaluate usability using cognitive walkthrough and think-aloud methods by performing task scenarios and assessments from the user’s perspective on the Flip application. The test was carried out on 100 respondents inthe Pekanbaru city area. Based on the test results, the effectiveness rate was 92.3% and the efficiency level was 0.22 goals/sec as well as the satisfaction level based on the method used value which can be seen from the problems caused are not included in the fatal category, and the positive response of respondents regarding the evaluation. This shows that the Flip application is very good but still needs improvement to be used more optimally. Recommendations are given as a reference in developing and improving applications in overcoming problems from the test results. Recommendations are interface improvements including appearance, indication, function, and operation of the Flip application.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116464125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive Intelligence for Healthcare Outcomes: An AI Architecture Overview 医疗保健结果的预测智能:AI架构概述

2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) Pub Date : 2023-04-21 DOI: 10.1109/ICSTSN57873.2023.10151477

Srinivasa Rao Burri, Abhishek Kumar, Anupam Baliyan, T. Kumar

引用次数: 3

Virtual Medical Assistant System for Diseases Detection using Machine Learning 基于机器学习的疾病检测虚拟医疗助理系统

2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) Pub Date : 2023-04-21 DOI: 10.1109/ICSTSN57873.2023.10151594

J. Kanimozhi, G. Preethi, N. Mohanasuganthi, S. A. Ayshwariya, Lijetha. C. Jaffrin

{"title":"Virtual Medical Assistant System for Diseases Detection using Machine Learning","authors":"J. Kanimozhi, G. Preethi, N. Mohanasuganthi, S. A. Ayshwariya, Lijetha. C. Jaffrin","doi":"10.1109/ICSTSN57873.2023.10151594","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151594","url":null,"abstract":"Health is very important for human lives. The health sector is growing day by day. Nowadays, due to information and communication technology, the health sector has grown immensely. Therefore, the virtual medical assistant system is developed by combining the machine learning algorithm to become part of this growth. The Virtual Medical Assistant System predicts heart disease according to the health parameters like age, height, weight, BMI, and hypertension given by the user. For pattern recognition and classification problems, K-Nearest Neighbor (k-NN), back-propagation neural networks, Naive Bayes, and Support Vector Machines (SVM) algorithms are employed to implement the system. The SVM classifier performs the best for the classification problems, and XGBoost is combined for feature extraction purposes to develop new feature combinations for training the SVM model. And from the experimental analysis, the accuracy obtained for SVM is 90.S%, but for others it’s less. This proposed system checks, whether a person is affected by the disease or not and recommends the right specialist for that disease, which is a difficult process nowadays. So it saves the patient both time and money. This work is to provide a virtual medical assistant that can analyze symptoms, predict diseases, and recommend a specialized doctor.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132323796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sugarcane Classification of Image Processing and Convolutional Neural Networks 甘蔗分类图像处理与卷积神经网络

2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) Pub Date : 2023-04-21 DOI: 10.1109/ICSTSN57873.2023.10151483

K. Kavitha, E. Umasankari

{"title":"Sugarcane Classification of Image Processing and Convolutional Neural Networks","authors":"K. Kavitha, E. Umasankari","doi":"10.1109/ICSTSN57873.2023.10151483","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151483","url":null,"abstract":"Sugarcane is a significant crop with several uses in the food, bio-energy, and bio-based product sectors. Many elements, including climate, soil fertility, and plant diseases, can have an impact on the quality of sugarcane. In this study, it suggest a sugarcane classification system based on convolutional neural networks (CNNs) and image processing to automatically evaluate sugarcane crops’ quality automatically. Using image processing techniques, the system uses digital photographs of sugarcane to extract attributes including color, texture, and form. These attributes are then sent into a CNN to be categorized into various grades or quality levels. By testing the proposed system using a sizable collection of photographs of sugarcane, comparing the outcomes to those attained using conventional visual inspection techniques. The findings indicate that the suggested system can classify sugarcane crops with a high degree of accuracy and dependability. The sugarcane industry may employ the suggested approach to increase the efficacy and precision of sugarcane quality evaluation. This research highlights the possibility of fusing deep learning models with image processing methods for agricultural applications.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126280992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A critical review on intrusion detection systems in IoT based on ML approach:A Survey 基于ML方法的物联网入侵检测系统综述

2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) Pub Date : 2023-04-21 DOI: 10.1109/ICSTSN57873.2023.10151604

N. Maheswaran, G. Logeswari, Shilpi Bose, T. Anitha

{"title":"A critical review on intrusion detection systems in IoT based on ML approach:A Survey","authors":"N. Maheswaran, G. Logeswari, Shilpi Bose, T. Anitha","doi":"10.1109/ICSTSN57873.2023.10151604","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151604","url":null,"abstract":"Internet of Things (IoT) is extensively applied in a number of domains such as environmental monitoring, industrial processes, home automation, healthcare etc. due to numerous advantages associated with it. However, it is challenging to overcome the security issues and ensure privacy in IoT environment. Intrusion Detection Systems (IDSs) is an important phenomenon that secures the networks and information systems. But conventional IDS techniques cannot be applied in IoT because of limited resources and heterogeneity since IoT has specific features such as protocol stacks, standards and resource-constrained devices. A survey is conducted in this paper upon IDS-based studies focusing IoT. The objective is to find the trends, assess the issues and explore future research opportunities. Further, the study also discusses about different classification methods for IDSs, possibilities for every attribute and analyzes the IDS methods. A taxonomy was proposed to classify the papers based on validation strategy, security threat, IDS placement strategy and detection method.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121228009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Media Multitasking with M-Learning Technology in Real-Time Classroom Learning: Analysing the Dynamics in Formal Educational Settings for the Future of E-Learning in India 实时课堂学习中使用移动学习技术的媒体多任务处理:分析印度电子学习未来正规教育环境中的动态

2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) Pub Date : 2023-04-21 DOI: 10.1109/ICSTSN57873.2023.10151509

Ashraf Alam

{"title":"Media Multitasking with M-Learning Technology in Real-Time Classroom Learning: Analysing the Dynamics in Formal Educational Settings for the Future of E-Learning in India","authors":"Ashraf Alam","doi":"10.1109/ICSTSN57873.2023.10151509","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151509","url":null,"abstract":"Teachers’ ability to integrate digital technology with formal pedagogical approaches is vital because it ensures that students retain the level of concentration required to understand complex arguments and conduct rigorous classroom-led research. The researcher here argues that classroom dynamics are increasingly at risk due to the addictive nature brought about by the pervasiveness of digital devices and social media, even though many studies have acknowledged the importance of technology in this media-rich world and the idea of “learn anytime, anywhere” associated with mobile learning. Due to the lack of comprehensive studies on the effects of technology on education, especially on the effects of “multitasking” on the teacher’s traditional roles as media orchestrators and learning facilitators, this article critically evaluates the existing literature on mobile learning. By discussing the pros and cons of incorporating technology into the traditional classroom, this article further attempts to give impetus to a discussion that has long been overdue","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114025454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0