Constanze Geyer, G. Aumayr, Yvonne Prinzellner, Ashraf Ragab, P. Panagiotidis, Kyriakos Giannakis, Alisa Simon, Nikos Angelopoulos, Angelos Liapis
{"title":"Disabilities in Evacuation for Cruise Ships - Leisure Lab Methodology to Support an Algorithm-driven Evacuation Prediction Model","authors":"Constanze Geyer, G. Aumayr, Yvonne Prinzellner, Ashraf Ragab, P. Panagiotidis, Kyriakos Giannakis, Alisa Simon, Nikos Angelopoulos, Angelos Liapis","doi":"10.1109/ICOCO53166.2021.9673552","DOIUrl":"https://doi.org/10.1109/ICOCO53166.2021.9673552","url":null,"abstract":"Although passengers on a cruise ship are a very heterogeneous group, improvements concerning evacuation procedures that consider people with a disability have been largely neglected in previous research. The PALAEMON project aims to help close this gap by developing a Decision Support System that improves and speeds up the evacuation process including people with disabilities. Therefore an explorative pilot study was conducted to measure the gait speed using a hemiparesis simulator. For the implementation of the pilot study - which we coined Leisure Lab - urban areas usually used for recreational activities were instrumentalized. A parkour with a total of 16 stations was set-up in a fun house in an amusement park in Vienna. Nine participants (4 male and 5 female participants) took part in the explorative study, completing two runs (one with and one without the hemiparesis simulator). The results showed a difference between the two runs of the participants. A loss of gait speed of up to 112% was measured. The results of the Leisure Lab study will be taken into account when developing the Decision Support System as well as for the development of further algorithms to decrease the time it takes for a full evacuation of a cruise ship.","PeriodicalId":262412,"journal":{"name":"2021 IEEE International Conference on Computing (ICOCO)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133962537","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":"Identified of Collision Alert in Vehicle Ad hoc based on Machine learning","authors":"M. Jasim, Nizar Zaghden, M. Bouhlel","doi":"10.1109/ICOCO53166.2021.9673495","DOIUrl":"https://doi.org/10.1109/ICOCO53166.2021.9673495","url":null,"abstract":"Traffic crashes and fatalities in school zones are common in urban areas. Vehicular Ad-hoc Networks (VANETs) can help prevent school zone traffic accidents by the delivery of warning messages to cars. Vehicle collision alert is a cost-effective method of managing traffic and reducing vehicle accidents. The use of roadside units to reduce automobile congestion in school zones is the aim of this essay (RSUs). It consists of three parts: one for managing collision alerts, another for clustering messages, and yet another for analyzing the cluster based on major and minor messages depending on the airbag sense data and vehicle lifetime. Using machine learning methods, this strategy collects, filters, and clusters communications. The K-means algorithm groups messages according to vehicle position, accident region, messages type, and collision type. The simulation findings reveal that when compared to earlier collision alarm systems, the proposed technique significantly improves the traffic management system.","PeriodicalId":262412,"journal":{"name":"2021 IEEE International Conference on Computing (ICOCO)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131792541","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":"IoT Based Smart Prediction System for Crop Suitability","authors":"S. Bevinakoppa, S. Padaganur, Vijay Nidagundi","doi":"10.1109/ICOCO53166.2021.9673499","DOIUrl":"https://doi.org/10.1109/ICOCO53166.2021.9673499","url":null,"abstract":"The majority of Indian population resides in villages and depends on the agriculture as their main source of income. Indian agriculture sector shares about 16% in the country's GDP. Day by day the urbanization and industrialization are occupying the lands of agriculture and thereby reducing the land available for the cultivation. In coming days, this may create food scarcity in the country. One of the most important problems faced by the farmers will be lack of identification of cultivation with an appropriate crop depending on the season and type of lands. Type of crops to be grown depends on land, temperature, humidity of the environment, soil moisture etc. Farmers will loose or reduced yield if an appropriate decision is not taken on type of crop. The objective of this proposed work is to develop an Internet of Things (IoT) based smart system that is capable of predicting the types of crops that can be cultivated in a given environmental condition and land. The environmental parameters can be obtained by using different sensors, even the soil moisture can be controlled automatically by controlling the water pump. This will improve by keeping the farmers with productive yields and keeps them motivated.","PeriodicalId":262412,"journal":{"name":"2021 IEEE International Conference on Computing (ICOCO)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128025486","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}
Chukwudi Festus Uwasomba, Yunli Lee, Zaharin Yusoff, C. Min
{"title":"FHKG: A Framework to Harvest Knowledge from Groupware Raw Data for AI","authors":"Chukwudi Festus Uwasomba, Yunli Lee, Zaharin Yusoff, C. Min","doi":"10.1109/ICOCO53166.2021.9673561","DOIUrl":"https://doi.org/10.1109/ICOCO53166.2021.9673561","url":null,"abstract":"In the era of textual data explosion, including due to a rising remote work culture, a system to harvest on-the-job knowledge of experts from groupware for AI enrichment has become one of the crucial technologies sought after in the field of knowledge technology. Most existing systems for knowledge harvesting are developed based on text corpora from the web, social media, newspapers and textbooks with little or no changeable modules and ontological representations. In this paper, we propose a deeper framework with changeable modules to acquire and represent knowledge from raw data in groupware discussions for AI. Such a framework can be implemented on any platform of choice using existing or newly designed modules that can be continually improved upon with higher sophistication or by added-value extensions. The framework is a formalisation of a semi-automated structure with reusable and incremental modules. The overall architecture of the framework is presented with evaluation results. The paper concludes by highlighting the proposed future developments within the framework.","PeriodicalId":262412,"journal":{"name":"2021 IEEE International Conference on Computing (ICOCO)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121089152","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}
Mohamad Amirul Syafiq Bin Peer Mohamed, D. P. Dahnil
{"title":"Development of Gesture-Based Women Safety Application","authors":"Mohamad Amirul Syafiq Bin Peer Mohamed, D. P. Dahnil","doi":"10.1109/ICOCO53166.2021.9673500","DOIUrl":"https://doi.org/10.1109/ICOCO53166.2021.9673500","url":null,"abstract":"Women safety is of major concern. The crime rates against women are increasing at an alarming rate and thus it is important to ensure their safety whether at home or at the workplaces. Many necessaries measures have been taken to ensure women safety. Despite many measure taken to protect women, there are still high number of crimes against them. In this research, a mobile based application is proposed that will help user to reach the relevant authorities during emergency situations in real time. The mobile application provides users with the fastest and simplest way to contact the nearest authorities by doing a few gestures on their smartphone. This application sends SMS to the emergency contacts number that has been registered in the phone contact, track one's location in real-time via GPS monitoring which uses MapBox as its primary source. Programming language that is Java and Firebase for the database are the technologies chosen to develop this application This application also can trigger alert sound by constant shaking of the phone to alert public once the victims encounter possible attackers. These features are for both everyday safety and real emergencies, making it an ultimate tool for all. With this application, victim not only can alert people in close proximity of their situations but get immediate help and action to prevent unwanted incidents from happening.","PeriodicalId":262412,"journal":{"name":"2021 IEEE International Conference on Computing (ICOCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125834053","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}