Proceedings of the 4th International Conference on Information Management & Machine Intelligence最新文献_第3页

A Comprehensive Study on the Association Between Personality Traits and Software Development 人格特质与软件开发关系的综合研究

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590900

Mitali Chugh, Apoorva Pandey, Sonali Vyas

引用次数: 0

Cross-Platform Empirical Analysis of Mobile Application Development frameworks: Kotlin, React Native and Flutter 移动应用开发框架的跨平台实证分析:Kotlin, React Native和Flutter

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590897

Bhawna Suri, Shweta Taneja, Isha Bhanot, Himanshi Sharma, Aanchal Raj

{"title":"Cross-Platform Empirical Analysis of Mobile Application Development frameworks: Kotlin, React Native and Flutter","authors":"Bhawna Suri, Shweta Taneja, Isha Bhanot, Himanshi Sharma, Aanchal Raj","doi":"10.1145/3590837.3590897","DOIUrl":"https://doi.org/10.1145/3590837.3590897","url":null,"abstract":"Mobile application development is currently seen as one of the most indispensable skills due to the recent boom in the population using smartphones and tablets. Due to the abundance and diversity of devices and platforms, developing applications for mobile devices is a complicated task involving multiple alternatives, technologies, and trade-offs. The development of applications in a cross-platform framework has become more prevalent in recent years. However, it is less clear which of the three major frame-works for android development, React Native, Flutter, and Kotlin is the most efficient in general comparisons. In this work, we have analyzed empirically the performance of three mobile application frameworks: Flutter, React-Native and Kotlin. Three tic-tac-toe games are designed using Flutter, React-Native and Kotlin, and their performance is compared on the basis of various parameters such as CPU usage, Memory usage, packages and their installations to understand the advantages and disadvantages of cross-platform and native mobile development. Our work also aims to assist developers in determining which framework is most suited to their requirements by comparing them in areas such as environment, development languages and ease of use. The results have indicated that Kotlin has a slight advantage over the two cross-platform technologies, Flutter and React Native. Memory usage of React-Native and Flutter were approximately 13.3% and 1.6% more than Kotlin respectively. Similarly, the application sizes of React-Native and Flutter are 98.06MB and 93.32 MB respectively, with Kotlin coming in last at 11.06 MB. In future, further investigation of the topics covered in this work is required to ensure and enhance the findings.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126396897","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

An Analytical Approach on Artificial Intelligence Based Models for Anomaly Detection in Sensor Networks 基于人工智能的传感器网络异常检测模型分析方法

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590906

A. R. Ugale, A. Potgantwar

{"title":"An Analytical Approach on Artificial Intelligence Based Models for Anomaly Detection in Sensor Networks","authors":"A. R. Ugale, A. Potgantwar","doi":"10.1145/3590837.3590906","DOIUrl":"https://doi.org/10.1145/3590837.3590906","url":null,"abstract":"Wireless sensor networks, also known as WSNs, have quickly transformed into one of the main important areas of research, significantly influencing the development of new technologies. Many resource-constrained sensor nodes function independently to collaborate and maintain wireless networks. Through these networks, essential source information is gathered and transferred to the end users or decision-makers. WSNs have been implemented in different critical applications, such as remote patient health monitoring systems, home automation systems, sales tracking systems, enemy target monitoring and tracking systems, and fire detection system implementations, where the trustworthiness of WSNs becomes very important. These kinds of applications desire to have both comprehensive and accurate data. WSNs may be prone to anomalies as a result of low-cost hardware and software that is unstable, as well as an adverse operating environment that may disrupt the network's communication. It is necessary to identify these anomalies because they can bring about malfunctions in the network and, as a result, impair the quality of the data that has been gathered. In this research, we investigate the anomalies present in WSN, discuss the desirable attributes of anomaly detection approaches, and examine the different anomaly detection strategies that can be applied to wireless sensor networks.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126485922","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 Review on Big Data Utilization in Metaverse World 元世界大数据利用研究综述

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590938

Akanksha Rai, Sonali Vyas

{"title":"A Review on Big Data Utilization in Metaverse World","authors":"Akanksha Rai, Sonali Vyas","doi":"10.1145/3590837.3590938","DOIUrl":"https://doi.org/10.1145/3590837.3590938","url":null,"abstract":"We are currently in the big data era. The Metaverse is an upcoming technology that combines big data, virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and other technologies to reduce the gap between online and offline contact. It wants to be a place where we may work, travel, communicate, shop, play, and mingling. When a person joins the Metaverse, they all engage with the virtual environment in some way using data. Data will continue to increase as the Metaverse is developed and used, creating a big data network that will put a tremendous amount of pressure on the digital world's process data ability. Therefore, one of the crucial technologies to construct the Metaverse is large data processing technology. In this study, we thoroughly analyze how Metaverse is transforming large data. Furthermore, we go into great length about the important security and privacy of Metaverse large data. Finally, we provide a summary of the Metaverse's current challenges and potential as well as its prospects for using big data. With this survey, we hope to give researchers ideas for future research and the potential of using big data in the Metaverse.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115869225","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

Interventive Educational System: Participation of Artificial Neural Network 干预式教育系统:人工神经网络的参与

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590941

P. Bhari, Geeta Tiwari

引用次数: 0

Industry 5.0: Sustainability Challenges in Fusion of Human and AI 工业5.0:人类与人工智能融合中的可持续性挑战

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590903

Amitabh Patnaik, Sunny Dawar, P-S. Kudal

{"title":"Industry 5.0: Sustainability Challenges in Fusion of Human and AI","authors":"Amitabh Patnaik, Sunny Dawar, P-S. Kudal","doi":"10.1145/3590837.3590903","DOIUrl":"https://doi.org/10.1145/3590837.3590903","url":null,"abstract":"Most industrial revolutions aim to separate human labor from that of machines. These machines are referred as \"machines\" or \"robots,\" will eventually take over the majority of the labor-intensive, monotonous, or hazardous tasks currently performed by people. Cleaning robots, for instance, can efficiently clean a room or an office. Future cleaning chores will eventually all be carried out by robots; it is only a question of time. Although currently both humans and robots can perform cleaning tasks, prior data on the rate of mechanization suggests that in the future humans will perform fewer cleaning than robots. The degree of technology and public acceptance of robots determine how quickly automation permeates human lives. Studying how people and robots interact and how it affects society is crucial because robots may have a big impact on civilization. Industry 4.0 has attracted a lot of interest since its debut in 2011. Additionally, the conversation around Industry 5.0 has already begun among some academics and futurists. They present various visions of Industry 5.0. Collaboration between robots and humans is one evolving topic for Industry 5.0. People are unsure of what Industry 5.0's theme will be after some time. However, people can be certain that the coworking of humans and robots will be a significant advancement for society and have a big impact on how people conduct business. Researchers aim at contrasting Industry 4.0 with Industry 5.0 perspectives. Further researchers concentrate on the challenges which people would face when co-working between humans and robots start. Researchers finish the study by briefly discussing potential areas for future investigation.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"650 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131910872","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

Blockchain and its dimensions: Future Security 区块链及其维度:未来安全

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590886

Geerija Lavania, G. Sharma

引用次数: 0

Deep Learning Model for Work-Life Balance Prediction for Working Women in IT Industry IT行业职业女性工作与生活平衡预测的深度学习模型

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590846

S. Paigude, Sajeeda Shikalgar

{"title":"Deep Learning Model for Work-Life Balance Prediction for Working Women in IT Industry","authors":"S. Paigude, Sajeeda Shikalgar","doi":"10.1145/3590837.3590846","DOIUrl":"https://doi.org/10.1145/3590837.3590846","url":null,"abstract":"Industry professionals predict that the use of artificial intelligence and machine learning will significantly impact people's lives in the near future. They believe that it will allow individuals and organizations to perform more efficiently by improving their processes and making them more effective at their jobs. Machine learning could help improve employees' work-life balance by allowing them to perform more efficiently at their jobs. It could also increase the general quality of their lives. What would be the outcome of an experiment involving machine learning to find ways to improve the balance between work and life? The study aimed to analyze individuals' subjective feelings about work-life balance. Through a machine learning method, the researchers could identify the characteristics influencing the balance between work and life. They then used a forecasting model to analyze the data and determine how much women in the IT industry observe a work-life balance. The study was conducted on 150 female IT professionals. The researchers used a questionnaire to gather information about the participants. They then used various deep-learning models to analyze the data. The researchers analyzed the models' performance by calculating the mean absolute and square errors. They found that the models known as the Multilayer Perceptron and the Long Short-term Memory performed well in terms of their accuracy when forecasting future business conditions. Comparing the two models revealed that the Short-Term Long Memory performed better than the MLP in terms of accuracy. This finding could be beneficial for studies related to the behavior of organizations.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128909401","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

Survey on Foraging Behavior Algorithms 觅食行为算法调查

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590890

S. Rathore, Gaurav Kumar Das, Sachinpal Singh Yadav, Surendra Yadav, A. Yadav

{"title":"Survey on Foraging Behavior Algorithms","authors":"S. Rathore, Gaurav Kumar Das, Sachinpal Singh Yadav, Surendra Yadav, A. Yadav","doi":"10.1145/3590837.3590890","DOIUrl":"https://doi.org/10.1145/3590837.3590890","url":null,"abstract":"This paper presents a study of research activities inspired by the foraging behavior of animals. Foraging behavior algorithms are computational models that simulate the behavior of organisms searching for food resources in their environment. Foraging behavior algorithms has made the researcher's to actively do research over few years due to their potential applications in fields such as robotics, optimization, and AI. This survey paper provides an overview in foraging behavior algorithms. It begins by defining the concept of foraging behavior and exploring its importance in the natural world. The paper then reviews different types of foraging behavior algorithms, consisting of ant colony optimization, particle swarm optimization. The paper also discusses various applications of foraging behavior algorithms in fields such as wireless sensor networks, and search and rescue operations. Furthermore, the survey paper examines some of the challenges and limitations of foraging behavior algorithms and provides suggestions for future research directions. Overall, this survey paper highlights the potential of foraging behavior algorithms to improve the efficiency and effectiveness of various computational systems and emphasizes the need for further research in this field to address some of the remaining challenges Additional Keywords and Phrases: Metaheuristic, Optimization, nature inspiration, foraging behavior, ABC, BCO, HBA.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126848416","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

Improving Multi-Channel Wireless Network Security Performance Using Trust-Enhanced Blockchain Deployment 使用增强信任的区块链部署改进多通道无线网络安全性能

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590951

Manisha Bhatnagar, Dolly Thankachan

引用次数: 0