{"title":"Impact of Telepresence of Hotel Websites on Behavioral Intention of Indian consumers: A Select Study","authors":"Utkal Khandelwal, Avnish Sharma, Aneesya Panicker","doi":"10.1109/ISCON57294.2023.10112008","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112008","url":null,"abstract":"With the advancement in technology and increasing innovation, the organizational ways of responding to customers’ needs, promoting and advertising the product and service brands has completely transformed. The different market changes and trends broadly impacted the marketing communication process of the organizations. In the world of competitions and technological growth the hotel industry also changed its way of approaching and targeting customers to build strong market presence. The development of virtual world in form of hotel websites and its associated customer experiences have strongly influenced the buying behavior and decision making process. This study talks about the different variables including sensory, cognitive and emotional attributes and their role in influencing customer buying intentions. The present study identifies the antecedents of telepresence by investigating variables including sensory, emotional and cognitive attributes and examines the impact of telepresence on behavioral intentions of customers towards hotel websites. An online self-administered survey was conducted to collect data. This study helps to determine the conceptual framework model fitness through confirmatory factor analysis. The findings of this study state the significance of telepresence theory by exploring various antecedents and outcomes of the customers’ telepresence in a hotel’s website context. It has concluded that higher telepresence leads to positive behavioral intention among customers.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114519699","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}
Kartik Tomar, Krishi Bisht, Kshitiz Joshi, R. Katarya
{"title":"Cyber Attack Detection in IoT using Deep Learning Techniques","authors":"Kartik Tomar, Krishi Bisht, Kshitiz Joshi, R. Katarya","doi":"10.1109/ISCON57294.2023.10111990","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10111990","url":null,"abstract":"The Internet of things (IoT) consists of millions of digital devices which interact with each other through minimum user interaction. IoT is one of the most rapidly expanding computing sectors; however, it is vulnerable to many attacks. An emerging concern in the Internet of Things (IoT) space is attack and strange placement on the IoT framework. Attacks and dangers on these systems are also growing proportionally because of the expanding IoT foundation usage across all industries. In this paper, a review of previous work is conducted, and several deep learning techniques are proposed for accurately predicting attacks on IoT systems. Injection attacks, Man-in-the-middle attacks, Information gathering, Malware attacks, and DDoS/Dos attacks are such attacks and irregularities that might occur in an IoT framework. Identifying such attacks and malicious traffic is important for the Internet of things (IoT) network to block unwanted traffic and unauthorized access. The Edge-IIoTset Cyber Security Dataset and the VGG16 and VGG19 algorithms are utilized to evaluate the effectiveness of the proposed solution; F1 score, precision, recall, and accuracy are the assessment metrics used.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116973656","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 and Scalability of E-Commerce Website by OWASP threats.","authors":"Mayank Srivastava, Animesh Raghuvanshi, Dhruv Khandelwal","doi":"10.1109/ISCON57294.2023.10111955","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10111955","url":null,"abstract":"In the current age of fast demanding services related to the E-Commercial commodities, it becomes the need of the hour to protect each and every detail of our customer and the website because after all, security of the data is our prime priority. Moreover this field has good set of areas in which extensive studies and research could be done. Each year we see thousands of local website and their data which is leaked or get encountered by some of top programmers. In this paper, we will try to secure our website from some of the top listed threats on OWASP (Open Web Application Security Project), which is also known as OWASP 10. The paper is a detailed analysis of how to secure our website from phishing attack, broken authentication, sensitive data exposure, XSS Attack, parameter tampering and SQL injection. In terms of scalability we will go with the technique of database replication and. To make our study effective we will be using various cheat sheets by OWASP and some tools like Jenkins, burp suite, network scanner, wire-shark.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123502789","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":"MCDM Computational Approaches for Green Supply Chain Management Strategies","authors":"Anand Jaiswal, Pushpa Negi, Nripendra Singh","doi":"10.1109/ISCON57294.2023.10112124","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112124","url":null,"abstract":"Rising environmental concerns due to global pollution conditions have intensified the need for eco-efficient strategies in every aspect of technology, engineering and management. This also necessitated the incorporation of environment-friendly green supply chain practices in an organization. A green supply chain management (GSCCM) problem includes multiple conflicting parameters such as pollution generation, cost, quality, time and others. To find an ideal solution in such conflicting multi-criteria problems in GSCM, researchers often use different Multi-Criteria Decision Making (MCDM) computations. This study provides an overview of various MCDM computational approaches and its usability in different issues under green supply chain area. The study provides an in-depth analysis of available recent research articles that uses different MCDM computational approaches. Classification of various issues coming under green supply chain problems is provided in the study, and use of MCDM computational approaches over such issues is discussed relying on the recent literature study. The study, towards the end, provides a SWOT analysis for different MCDM computational approaches in reference to GSCM problems.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123644217","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":"Lips and Tongue Cancer Classification Using Deep Learning Neural Network","authors":"Satish Bansal, R. S. Jadon, S. K. Gupta","doi":"10.1109/ISCON57294.2023.10112158","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112158","url":null,"abstract":"One of the major diseases in developing countries is Oral Cancer, caused by alcohol, tobacco product and smoking which creates uncontrolled and abnormal cells in parts of human body. Recent Convolution Neural Network (CNN) has helped in medical industry to used medical images for finding the different types of diseases. The objective of research to build new CNN model which use for analysis the oral cancer images and determine the cancerous and noncancerous image. In this paper, CNN technique and image processing are used to categorize cancer or non-cancer lips and tongue image dataset. Deep Learning approaches is used to develop and check the performance of proposed CNN model. For experiment small Kaggle dataset that contains cancerous and non-cancerous lips and tongue images and apply proposed CNN model (Oral_Cancer_Detection). The result of Oral_Cancer_Detection model is very effective and accurate with low complexity. The Oral_Cancer_Detection model found 94% accuracy in our experiment and achieved better accuracy results in less time.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121888431","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":"Interpretation and Analysis of Machine Learning Models for Brain Stroke Prediction","authors":"Ritesh Kumari, Hitendra Garg","doi":"10.1109/ISCON57294.2023.10112188","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112188","url":null,"abstract":"Brain stroke is a significant cause of death nowadays. As per WHO, 11% of the population dies yearly from it. However, early measures can save many lives. Machine Learning (ML) is used as a tool for early predictions in a human through their symptoms, lifestyle, and from medical history. With the advancement in machine learning, features responsible for brain stroke can be identified and ranked as per their effect. Such features for brain stroke are hypertension, smoking status, heart disease, body mass index, and sugar level. In this paper, various ML classifiers such as Neural Network (NN), Support Vector Machine (SVM), Random Forest (RMF), Decision Tree (DST), and Gradient Boost (GBST) are used to classify patients with brain stroke. The models are then compared for the best results. Lastly, Local Interpretable Model-agnostic Explanation (LIME) and SHAP (SHapley Additive exPlanations) are used for explanation to find the reason behind the decision taken by the best ML model. The results show that RMF (GBST after that) achieves the highest prediction accuracy.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121897323","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 and Performance Evaluation of Load Balanced Routing in SDN based Fat Tree Data Center","authors":"Raj P. Dhanya, V. Anitha","doi":"10.1109/ISCON57294.2023.10112200","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112200","url":null,"abstract":"As the number of data intensive and distributed applications increase, data centers are highly utilized for their storage, processing and accessing. These applications cause throughput sensitive incast traffic in the data center. Traditional shortest path routing algorithms cause traffic hotspots in the shortest path links and lead to congestion. Therefore, load balanced routing strategies need to be employed to spread the traffic across multiple paths. This work employs the idea of multi path routing to achieve load balancing in SDN based Fat tree Data center. It implements Equal Cost Multi Path routing algorithm on Fat tree Data Center Network in Ryu SDN controller and evaluates its load balancing performance against traditional Shortest Path Routing algorithm under different data center benchmark traffic scenarios. We have conducted an exhaustive study about the load balancing effect of ECMP on Ryu based SDN Fat tree data center and found that ECMP increases Average network throughput by 68% and Average Link utilization by 60%. Also packet drop is decreased by 44% with ECMP.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122072574","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":"Study on Deep Learning Models for Human Pose Estimation and its Real Time Application","authors":"Jyoti Jangade, K. S. Babulal","doi":"10.1109/ISCON57294.2023.10112004","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112004","url":null,"abstract":"In computer vision, human pose estimation details the posture of the person’s body structure that can be Kinematic, Planer, and Volumetric in an image or video. However, pose detection is often critical to be driven by distinct human actions. Thus, this survey report analysis the recent progression of the bottom-up and top-down human pose evaluation models. This survey report focuses on 2D and 3D skeleton-based human pose detection from the captured Red Green Blue(RGB) images. We have condensed the performance of the recent pose recognition, tracking, and detection techniques that utilize pose estimation from colour images as captured and then exhibit room for much more refinement in this domain. In this paper, scrutinize the study of human pose estimation models like 2d and 3d HPE for identify human movements such as running, dancing, sport so on and recent computer vision-based advances. This study has included various methods for detecting in two and three dimensions. This paper summarises the deep learning models for HPE, dataset, and challenges.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123996507","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}
Ankush Agarwal, Brijesh Kumar Gupta, Kailash Kumar, R. Agrawal
{"title":"A Neural Network based Concept to Improve Downscaling Accuracy of Coarse Resolution Satellite Imagery for Parameter Extraction","authors":"Ankush Agarwal, Brijesh Kumar Gupta, Kailash Kumar, R. Agrawal","doi":"10.1109/ISCON57294.2023.10112108","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112108","url":null,"abstract":"In countries where most of the economy depends on agriculture, the agriculture has to be observed closely because there is a need to timely monitoring the agriculture for better productivity that leads in good economy. Various parameters like land surface temperature (LST), soil moisture, precipitation, vegetation indices, humidity, etc should be monitored timely, as they directly or indirectly affect the agriculture that affect the economy. Here we are trying to downscale the LST extracted from the MODIS data. The challenge here is to handle, process, and downscale the data from MODIS low resolution (1000m) to high resolution (30m) equivalent to the Landsat. For this purpose, a back propagation neural network is used. The neural network is trained using downscaled MODIS LST data as input to provide a LANDSAT equivalent output at 30m resolution. RMSE is computed as an indicator of the performance of the approach.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125907423","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}
Efendi, A. Gui, Gabriel Michael Ivan Santosa, A. A. Pitchay, Christopher Jourdan, Sudiana
{"title":"The Effect of Information System Success Model, Information Security, and Customer Satisfactions on Digital Bank Applications","authors":"Efendi, A. Gui, Gabriel Michael Ivan Santosa, A. A. Pitchay, Christopher Jourdan, Sudiana","doi":"10.1109/ISCON57294.2023.10111969","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10111969","url":null,"abstract":"The digital bank services can be defined as banking services or activities using electronic or digital facilities owned by the Bank, and/or through digital media owned by prospective customers and/or Bank customers, which are carried out independently. The objective of research is to analyze the factors influencing customer satisfaction of digital banks in Indonesia, because in recent years the adoption process for digital banks in Indonesia has been quite massive by offering unique advantages in each application. This research is involving Information System Success Model, Information Security, and Customer Satisfaction. Then, the research was conducted using a quantitative method by distributing questionnaires on Google Forms through social media within three weeks and obtaining a total of 250 respondents. Data processing is carried out through SMART PLS by involving PLS-SEM and Bootstrapping Analysis. The results of the study states that customer satisfaction is influenced by information quality, perceived security, and perceived privacy. In contrast with that, service quality and system quality do not able to support customer satisfaction. Through this research, we suggest that digital bank management should optimize the information quality, perceived security, and perceived privacy because there is a significant relationship on customer satisfaction.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128369047","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}