Pub Date : 2023-02-11DOI: 10.1109/ICITIIT57246.2023.10068710
Jafseer K T, S. S, S. A.
As data is ubiquitous in several real-world problems, data stream mining is a rapidly growing research area. It is expected that data stream sources will undergo changes in data distribution due to their ephemeral nature, which is called concept drift. There has been a very scant study of one particular type of drift, namely feature drift, so this paper aims to explore that type of drift. As a result of feature drift, learners must detect and adapt to changes in the relevant subset of features and the changing nature of the learning task itself. An approach to detecting feature drift was developed in this work. We used overlapping landmark windowing to keep the previous data's properties windows while analyzing the most recent data. Using the Mann-Whitney U test, we compare and store the distribution of each feature in two consecutive windows. Whenever the statistical properties of the window exclude a particular boundary from the distribution, drift is detected. We validated the effectiveness of our proposal by conducting experiments on real data.
{"title":"Feature Drift Detection using Overlapping Window and Mann-Whitney U Test","authors":"Jafseer K T, S. S, S. A.","doi":"10.1109/ICITIIT57246.2023.10068710","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068710","url":null,"abstract":"As data is ubiquitous in several real-world problems, data stream mining is a rapidly growing research area. It is expected that data stream sources will undergo changes in data distribution due to their ephemeral nature, which is called concept drift. There has been a very scant study of one particular type of drift, namely feature drift, so this paper aims to explore that type of drift. As a result of feature drift, learners must detect and adapt to changes in the relevant subset of features and the changing nature of the learning task itself. An approach to detecting feature drift was developed in this work. We used overlapping landmark windowing to keep the previous data's properties windows while analyzing the most recent data. Using the Mann-Whitney U test, we compare and store the distribution of each feature in two consecutive windows. Whenever the statistical properties of the window exclude a particular boundary from the distribution, drift is detected. We validated the effectiveness of our proposal by conducting experiments on real data.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115425774","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}
Pub Date : 2023-02-11DOI: 10.1109/ICITIIT57246.2023.10068564
S. Rajput, Anvita Jadhav, Janhavi Gadge, Diya Tilani, Vaibhav Dalgade
For the past few years, as the demand for food has increased due to population, Food security has emerged as a major issue, because many intermediaries alter products to gain profits which in turn degrades the quality of the product and affects the health of the population. The current agricultural food supply chains have a number of significant issues, including plenty of participants, poor communication driven by lengthy supply chains, distrust between members, and centralized systems. Developing a traceability system for the agricultural food supply chain becomes more and more important as traditional agri-food logistics patterns can no longer meet market needs. We can build a system that keeps track of the product quality and other factors throughout the supply chain by using Blockchain technology along with various other technologies like sensors which are used to gather data from the growing stages of the product, IPFS which stores this data securely at one place and the entire data can be accessed by the consumer through RFID tags. This data can be accessed by the consumer who can verify the quality of the product. This can help maintain trust in the supply chain. In this paper, we have summarized a few previous related research and proposed a system for traceability in the agricultural supply chain.
{"title":"Agricultural Food supply chain Traceability using Blockchain","authors":"S. Rajput, Anvita Jadhav, Janhavi Gadge, Diya Tilani, Vaibhav Dalgade","doi":"10.1109/ICITIIT57246.2023.10068564","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068564","url":null,"abstract":"For the past few years, as the demand for food has increased due to population, Food security has emerged as a major issue, because many intermediaries alter products to gain profits which in turn degrades the quality of the product and affects the health of the population. The current agricultural food supply chains have a number of significant issues, including plenty of participants, poor communication driven by lengthy supply chains, distrust between members, and centralized systems. Developing a traceability system for the agricultural food supply chain becomes more and more important as traditional agri-food logistics patterns can no longer meet market needs. We can build a system that keeps track of the product quality and other factors throughout the supply chain by using Blockchain technology along with various other technologies like sensors which are used to gather data from the growing stages of the product, IPFS which stores this data securely at one place and the entire data can be accessed by the consumer through RFID tags. This data can be accessed by the consumer who can verify the quality of the product. This can help maintain trust in the supply chain. In this paper, we have summarized a few previous related research and proposed a system for traceability in the agricultural supply chain.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128991303","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}
Pub Date : 2023-02-11DOI: 10.1109/ICITIIT57246.2023.10068711
S. Kahate, A. D. Raut
Identification of cyberbullying and cyberstalking for real-time use cases is a multi domain task that involves the design of social media data extraction, sentiment analysis, sentiment pattern evaluation, and regression models. To perform this task, researchers have proposed the use of high-density feature representation models that can extract social media sentiments, and combine them with user specific parameters like age, gender, time of post, etc. But existing models are either non-comprehensive or capable of achieving limited accuracy when used for real-time scenarios. Moreover, these models are not flexible to multimodal inputs, which further limits their scalability levels. To address these concerns, this paper proposes the development of a deep learning model for cyberbullying and cyberstalking attack mitigation via social media analysis. The proposed model initially collects tweets posted by users, extracts meta data, and analyzes language features for training a Long-Short-Term Memory (LSTM) based Convolutional Neural Network (CNN), which assists in the pre-filtering of tweets. The filtered tweets are passed through a Natural Language Processing (NLP) engine that assists in sentiment identification for these texts. Sentiment data and Word Embedding capabilities are used to anticipate cyberbullying and cyberstalking attacks. This is done via CNN based pattern analysis, which assists in the efficient identification and mitigation of these attacks. Due to the integration of these models, the proposed method is able to improve attack detection accuracy by 3.5 %, while reducing the identification delay by 8.3 % in real-time scenarios.
{"title":"Design of a Deep Learning Model for Cyberbullying and Cyberstalking Attack Mitigation via Online Social Media Analysis","authors":"S. Kahate, A. D. Raut","doi":"10.1109/ICITIIT57246.2023.10068711","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068711","url":null,"abstract":"Identification of cyberbullying and cyberstalking for real-time use cases is a multi domain task that involves the design of social media data extraction, sentiment analysis, sentiment pattern evaluation, and regression models. To perform this task, researchers have proposed the use of high-density feature representation models that can extract social media sentiments, and combine them with user specific parameters like age, gender, time of post, etc. But existing models are either non-comprehensive or capable of achieving limited accuracy when used for real-time scenarios. Moreover, these models are not flexible to multimodal inputs, which further limits their scalability levels. To address these concerns, this paper proposes the development of a deep learning model for cyberbullying and cyberstalking attack mitigation via social media analysis. The proposed model initially collects tweets posted by users, extracts meta data, and analyzes language features for training a Long-Short-Term Memory (LSTM) based Convolutional Neural Network (CNN), which assists in the pre-filtering of tweets. The filtered tweets are passed through a Natural Language Processing (NLP) engine that assists in sentiment identification for these texts. Sentiment data and Word Embedding capabilities are used to anticipate cyberbullying and cyberstalking attacks. This is done via CNN based pattern analysis, which assists in the efficient identification and mitigation of these attacks. Due to the integration of these models, the proposed method is able to improve attack detection accuracy by 3.5 %, while reducing the identification delay by 8.3 % in real-time scenarios.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121802582","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}
Pub Date : 2023-02-11DOI: 10.1109/ICITIIT57246.2023.10068609
Abhishek Verma, Virendar Ranga, D. Vishwakarma
In last few decades one of the major problems is air pollution which has raised the eyebrows of everyone. Despite all the efforts, it still lies in the category of dangerous. In air pollution there is one of the most hazardous gases named carbon monoxide which is a matter of concern & produced mostly whenever a material burns with a lack of oxygen. This paper presents the forecasting of carbon monoxide with the help of a satellite-based sentinel 5p dataset using earth engine. Further, with the help of the deep learning approach ‘LSTM’, we forecast a time series base result. We have trained and tested the data using a deep-learning model. We have evaluated the potential results by overlapping the original and predicated values and calculating Root-mean-square (RMS) error to validate our approach. The results show that the method of LSTM is very efficient and accurate.
{"title":"Forecasting of Satellite Based Carbon-Monoxide Time-Series Data Using a Deep Learning Approach","authors":"Abhishek Verma, Virendar Ranga, D. Vishwakarma","doi":"10.1109/ICITIIT57246.2023.10068609","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068609","url":null,"abstract":"In last few decades one of the major problems is air pollution which has raised the eyebrows of everyone. Despite all the efforts, it still lies in the category of dangerous. In air pollution there is one of the most hazardous gases named carbon monoxide which is a matter of concern & produced mostly whenever a material burns with a lack of oxygen. This paper presents the forecasting of carbon monoxide with the help of a satellite-based sentinel 5p dataset using earth engine. Further, with the help of the deep learning approach ‘LSTM’, we forecast a time series base result. We have trained and tested the data using a deep-learning model. We have evaluated the potential results by overlapping the original and predicated values and calculating Root-mean-square (RMS) error to validate our approach. The results show that the method of LSTM is very efficient and accurate.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131050557","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}
Pub Date : 2023-02-11DOI: 10.1109/ICITIIT57246.2023.10068672
Akshay Suryavanshi, A. G, M. N, Rishika M, Abdul Haq N
A decentralized and secure architecture made possible by blockchain technology is what Web 3.0 is known for. By offering a secure and trustworthy platform for transactions and data storage, this new paradigm shift in the digital world promises to transform the way we interact with the internet. Data is the new oil, thus protecting it is equally crucial. The foundation of the web 3.0 ecosystem, which provides a secure and open method of managing user data, is blockchain technology. With the launch of Web 3.0, demand for seamless communication across numerous platforms and technologies has increased. Blockchain offers a common framework that makes it possible for various systems to communicate with one another. The decentralized nature of blockchain technology almost precludes hacker access to the system, ushering in a highly secure Web 3.0. By preserving the integrity and validity of data and transactions, blockchain helps to build trust in online transactions. AI can be integrated with blockchain to enhance its capabilities and improve the overall user experience. We can build a safe and intelligent web that empowers users, gives them more privacy, and gives them more control over their online data by merging blockchain and AI. In this article, we emphasize the value of blockchain and AI technologies in achieving Web 3.0's full potential for a secure internet and propose a Blockchain and AI empowered framework. The future of technology is now driven by the power of blockchain, AI, and web 3.0, providing a secure and efficient way to manage digital assets and data.
{"title":"The integration of Blockchain and AI for Web 3.0: A security Perspective","authors":"Akshay Suryavanshi, A. G, M. N, Rishika M, Abdul Haq N","doi":"10.1109/ICITIIT57246.2023.10068672","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068672","url":null,"abstract":"A decentralized and secure architecture made possible by blockchain technology is what Web 3.0 is known for. By offering a secure and trustworthy platform for transactions and data storage, this new paradigm shift in the digital world promises to transform the way we interact with the internet. Data is the new oil, thus protecting it is equally crucial. The foundation of the web 3.0 ecosystem, which provides a secure and open method of managing user data, is blockchain technology. With the launch of Web 3.0, demand for seamless communication across numerous platforms and technologies has increased. Blockchain offers a common framework that makes it possible for various systems to communicate with one another. The decentralized nature of blockchain technology almost precludes hacker access to the system, ushering in a highly secure Web 3.0. By preserving the integrity and validity of data and transactions, blockchain helps to build trust in online transactions. AI can be integrated with blockchain to enhance its capabilities and improve the overall user experience. We can build a safe and intelligent web that empowers users, gives them more privacy, and gives them more control over their online data by merging blockchain and AI. In this article, we emphasize the value of blockchain and AI technologies in achieving Web 3.0's full potential for a secure internet and propose a Blockchain and AI empowered framework. The future of technology is now driven by the power of blockchain, AI, and web 3.0, providing a secure and efficient way to manage digital assets and data.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127638583","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}
Pub Date : 2023-02-11DOI: 10.1109/ICITIIT57246.2023.10068636
K. Shashvat, A. Kaur
In tropical nations, dengue fever is one of the most widespread vector-borne infections, particularly in developing countries such as India, Bangladesh, and Pakistan. Dengue fever can range from mild to severe fever cases. Dengue fever is an epidemic spread by mosquitos that affects people of all ages in over a hundred countries throughout the world. The research examines real-time series prediction and analysis using three regression models, as well as the development of a weighted average prediction model for infectious illness prediction. The integrated diseases monitoring programme of the Government of India provided monthly statistics on dengue cases from 2014 to 2017. Three regression models were used to analyse data: support vector regression, neural network, and linear regression. Mean Absolute Error, Root Mean Square Error, and Mean Square Error are some of the performance criteria that have been employed. In terms of its effectiveness, it was discovered that the postulated weighted ensemble model performed better. The primary purpose of this project is to reduce prediction errors, and we discovered that our planned weighted ensemble model is more effective in this regard.
{"title":"A Weighted Ensemble Model for Prediction of Dengue Occurrence in North India (Chandigarh)","authors":"K. Shashvat, A. Kaur","doi":"10.1109/ICITIIT57246.2023.10068636","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068636","url":null,"abstract":"In tropical nations, dengue fever is one of the most widespread vector-borne infections, particularly in developing countries such as India, Bangladesh, and Pakistan. Dengue fever can range from mild to severe fever cases. Dengue fever is an epidemic spread by mosquitos that affects people of all ages in over a hundred countries throughout the world. The research examines real-time series prediction and analysis using three regression models, as well as the development of a weighted average prediction model for infectious illness prediction. The integrated diseases monitoring programme of the Government of India provided monthly statistics on dengue cases from 2014 to 2017. Three regression models were used to analyse data: support vector regression, neural network, and linear regression. Mean Absolute Error, Root Mean Square Error, and Mean Square Error are some of the performance criteria that have been employed. In terms of its effectiveness, it was discovered that the postulated weighted ensemble model performed better. The primary purpose of this project is to reduce prediction errors, and we discovered that our planned weighted ensemble model is more effective in this regard.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133799629","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}
Pub Date : 2023-02-11DOI: 10.1109/ICITIIT57246.2023.10068691
M. Mallikarjuna, Aditya Bhosle
Driver behaviour is a significant factor in the smooth driving of vehicles on the roads. 94 % of crashes and road accidents are prone to drivers' rash driving behaviour. To address issues related to road accidents and crashing of vehicles on the road, Highly Automated Vehicle (HAV) Technologies have been proposed. Self-Driving Cars are part of Highly Automated Tech-nologies having promising benefits ranging from Greater Road Safety, Greater Independence, Saving money, More Productivity, Reduced Congestion and Green House Gains. The current study focuses on the deployment of self-driving automobiles based on the Deep Learning paradigm. The automobile has been simulated on the Udacity simulator for convenience and safety. On the Udacity platform, a technique for training and simulating an unmanned vehicle model using a convolutional neural network has been developed. The data used to train the model is captured in the simulator and fed as input into the Deep CNN. Following data collection, Deep CNN is trained to have Safety Navigation by passing Steering, Throttle, Brake and Speed as Control Inputs. The use of three cameras considerably improves the precision of the navigation job. To manage the car, the steering wheel amount will be modified such that it runs in the centre of the lane. We evaluated the model using UDACITY's simulation system. The proposed model has been evaluated considering the-No of epochs vs loss calculation, as performance metrics, and was found that the proposed model has shown superiority with the existing works.
{"title":"Self-Driving Car: Simulation of Highly Automated Vehicle Technology using Convolution Neural Networks","authors":"M. Mallikarjuna, Aditya Bhosle","doi":"10.1109/ICITIIT57246.2023.10068691","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068691","url":null,"abstract":"Driver behaviour is a significant factor in the smooth driving of vehicles on the roads. 94 % of crashes and road accidents are prone to drivers' rash driving behaviour. To address issues related to road accidents and crashing of vehicles on the road, Highly Automated Vehicle (HAV) Technologies have been proposed. Self-Driving Cars are part of Highly Automated Tech-nologies having promising benefits ranging from Greater Road Safety, Greater Independence, Saving money, More Productivity, Reduced Congestion and Green House Gains. The current study focuses on the deployment of self-driving automobiles based on the Deep Learning paradigm. The automobile has been simulated on the Udacity simulator for convenience and safety. On the Udacity platform, a technique for training and simulating an unmanned vehicle model using a convolutional neural network has been developed. The data used to train the model is captured in the simulator and fed as input into the Deep CNN. Following data collection, Deep CNN is trained to have Safety Navigation by passing Steering, Throttle, Brake and Speed as Control Inputs. The use of three cameras considerably improves the precision of the navigation job. To manage the car, the steering wheel amount will be modified such that it runs in the centre of the lane. We evaluated the model using UDACITY's simulation system. The proposed model has been evaluated considering the-No of epochs vs loss calculation, as performance metrics, and was found that the proposed model has shown superiority with the existing works.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115234569","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}
Pub Date : 2023-02-11DOI: 10.1109/ICITIIT57246.2023.10068718
Akanksha Kapruwan, Sachin Sharma, H. Goyal
Many sectors are increasingly utilizing advanced expert system techniques to provide trustworthy answers for a range of CIN-C issues. Gaining access to prompt, high-quality medical care is one of the challenges that today's underdeveloped countries face. This is risky for patients' health. Accurate medical diagnosis is one of the most crucial steps to maintaining great health and living a long life. Adopting an expert system in diagnostic digital cytopathology system for CIN detection primary goal is to aid medical professionals in establishing diagnosis while also taking into account the information and symptoms at hand.
{"title":"Expert System Techniques in Intelligent Diagnostic Digital Cytopathology System for Cervical Intraepithelial Neoplasia Detection","authors":"Akanksha Kapruwan, Sachin Sharma, H. Goyal","doi":"10.1109/ICITIIT57246.2023.10068718","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068718","url":null,"abstract":"Many sectors are increasingly utilizing advanced expert system techniques to provide trustworthy answers for a range of CIN-C issues. Gaining access to prompt, high-quality medical care is one of the challenges that today's underdeveloped countries face. This is risky for patients' health. Accurate medical diagnosis is one of the most crucial steps to maintaining great health and living a long life. Adopting an expert system in diagnostic digital cytopathology system for CIN detection primary goal is to aid medical professionals in establishing diagnosis while also taking into account the information and symptoms at hand.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124445205","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}
Pub Date : 2023-02-11DOI: 10.1109/ICITIIT57246.2023.10068674
Farhah Qonita, Muhammad Fariz Budiman, Valina Mayang Sari, Natalia Limantara
The Ministry of Education, Culture, Research, and Technology is a ministry within the Government of Indonesia which administers affairs in the fields of preschool children's education, elementary education, secondary education, vocational education, higher education, cultural management, research, and the growth of technology. Universities that are going to open a new study program must submit a proposal to the Ministry of Education, Culture, Research, and Technology. The process of submitting this proposal is submitted through a system called SIAGA. This information system is used by universities to submit proposals and reviewers to evaluate and provide an assessment of the proposals submitted. The purpose of this research is to evaluate the usability of the SIAGA system. The evaluation is focused on from the reviewer's side because the reviewer is the main user of this system. The result of this study is performance assessment technique demonstrate that application pages are generally effective and pleased with the application system, but application is still inefficient in terms of evaluating characteristics. Improvements in this study focused on improving page layout and information navigation instructions when using the application.
{"title":"Analysis of User Experience on The Government Application of Indonesian Higher Education Institutional Information Systems Using Usability Method","authors":"Farhah Qonita, Muhammad Fariz Budiman, Valina Mayang Sari, Natalia Limantara","doi":"10.1109/ICITIIT57246.2023.10068674","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068674","url":null,"abstract":"The Ministry of Education, Culture, Research, and Technology is a ministry within the Government of Indonesia which administers affairs in the fields of preschool children's education, elementary education, secondary education, vocational education, higher education, cultural management, research, and the growth of technology. Universities that are going to open a new study program must submit a proposal to the Ministry of Education, Culture, Research, and Technology. The process of submitting this proposal is submitted through a system called SIAGA. This information system is used by universities to submit proposals and reviewers to evaluate and provide an assessment of the proposals submitted. The purpose of this research is to evaluate the usability of the SIAGA system. The evaluation is focused on from the reviewer's side because the reviewer is the main user of this system. The result of this study is performance assessment technique demonstrate that application pages are generally effective and pleased with the application system, but application is still inefficient in terms of evaluating characteristics. Improvements in this study focused on improving page layout and information navigation instructions when using the application.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124561813","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}
Pub Date : 2023-02-11DOI: 10.1109/ICITIIT57246.2023.10068585
Ashwitha Noble P, Hubert Veyannie V, S. H
A practice known as URL phishing involves cybercriminals creating fake websites in order to lure victims and steal sensitive information. The attacker disguises themselves in an email, instant message, or text message, pose as a reliable source to persuade the recipient to open it. Once the recipient clicks the link, they realize they were deceived into clicking a potentially dangerous link. In this case, ransomware can be set up on the recipient's PC using a malware that can be used to lock it down or even worse the private data can be released. In spite of the fact that fraudulent websites often resemble the real thing, checking for warning signals alone is not enough to prevent URL phishing. A study found that around 90% of the data breach is due to phishing. The variety of phishing schemes has expanded over the years and they may now be more dangerous than ever before.
{"title":"Phishing Perception and Prediction","authors":"Ashwitha Noble P, Hubert Veyannie V, S. H","doi":"10.1109/ICITIIT57246.2023.10068585","DOIUrl":"https://doi.org/10.1109/ICITIIT57246.2023.10068585","url":null,"abstract":"A practice known as URL phishing involves cybercriminals creating fake websites in order to lure victims and steal sensitive information. The attacker disguises themselves in an email, instant message, or text message, pose as a reliable source to persuade the recipient to open it. Once the recipient clicks the link, they realize they were deceived into clicking a potentially dangerous link. In this case, ransomware can be set up on the recipient's PC using a malware that can be used to lock it down or even worse the private data can be released. In spite of the fact that fraudulent websites often resemble the real thing, checking for warning signals alone is not enough to prevent URL phishing. A study found that around 90% of the data breach is due to phishing. The variety of phishing schemes has expanded over the years and they may now be more dangerous than ever before.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130506815","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}
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