The Impact of Artificial intelligence and Robotics on the Future Employment Opportunities

Shaukat, Kamran; Iqbal, Farhat; Alam, Talha Mahboob; Aujla, Gagandeep Kaur; Devnath, Liton; Khan, Abdul Ghaffar; Iqbal, Rimsha; Shahzadi, Irum; Rubab, Afifah

doi:10.17352/tcsit.000022

Cited by 22 publications

(11 citation statements)

References 13 publications

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“…However, this can restrict its practical application. The author in [19,20] suggests the reversible data transformation (RDT) algorithm-based data gathering protocol. Their protocol eliminates the need for a private channel and doesn't rely on external authentication to preserve privacy against processing outside its intended use.…”

Section: Related Workmentioning

confidence: 99%

Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

Almazroi¹,

Eltoukhy²

2023

Computers, Materials &Amp; Continua

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Smartphones have now become an integral part of our everyday lives. User authentication on smartphones is often accomplished by mechanisms (like face unlock, pattern, or pin password) that authenticate the user's identity. These technologies are simple, inexpensive, and fast for repeated logins. However, these technologies are still subject to assaults like smudge assaults and shoulder surfing. Users' touch behavior while using their cell phones might be used to authenticate them, which would solve the problem. The performance of the authentication process may be influenced by the attributes chosen (from these behaviors). The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of smartphones. We began by concentrating on feature selection methods utilizing the grey wolf optimization strategy. The random forest classifier is used to evaluate these tactics. The testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public dataset. It achieved a 97.89% accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly; processing power of the device and memory to validate individuals. Simultaneously, the findings revealed that our approach has a lower equal error rate (EER) of 0.5104, a false acceptance rate (FAR) of 1.00, and a false rejection rate (FRR) of 0.0209 compared to the methods discussed in the literature. These promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.

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Section: Related Workmentioning

confidence: 99%

Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

Almazroi¹,

Eltoukhy²

2023

Computers, Materials &Amp; Continua

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“…SVM has O(n2), and it is considered as time complexity. The model complexity of a decision tree, one of the basic methods of a tree-based model, is O(mn2) [52]. The complexity of random forest is O(Mmn log n).…”

Section: Time Complexity and Model Evaluationmentioning

confidence: 99%

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

et al. 2021

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With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing companies attempt to optimize production efficiency. In this study, we investigated whether machine learning has explanatory power for quality prediction problems in the injection molding industry. One concern in the injection molding industry is how to predict, and what affects, the quality of the molding products. While this is a large concern, prior studies have not yet examined such issues especially using machine learning techniques. The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction. Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that autoencoder outperforms comparing accuracy, precision, recall, and F1-score. Feature importance tests also revealed that temperature and time are influential factors that affect the quality. These findings have strong implications for enhancing sustainability in the injection molding industry. Sustainable management in Industry 4.0 requires adapting artificial intelligence techniques. In this manner, this article may be helpful for businesses that are considering the significance of machine learning algorithms in their manufacturing processes.

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“…This research is motivated by the potential impact of intelligent robotics on various industries and society [13,14] and the need to stay up to date with the latest trends and their potential implications. It is necessary to understand the current state of technology and its challenges to inform future research and development efforts.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Robotics—A Systematic Review of Emerging Technologies and Trends

Licardo,

Domjan,

Orehovački

2024

Electronics

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Intelligent robotics has the potential to revolutionize various industries by amplifying output, streamlining operations, and enriching customer interactions. This systematic literature review aims to analyze emerging technologies and trends in intelligent robotics, addressing key research questions, identifying challenges and opportunities, and proposing the best practices for responsible and beneficial integration into various sectors. Our research uncovers the significant improvements brought by intelligent robotics across industries such as manufacturing, logistics, tourism, agriculture, healthcare, and construction. The main results indicate the importance of focusing on human–robot collaboration, ethical considerations, sustainable practices, and addressing industry-specific challenges to harness the opportunities presented by intelligent robotics fully. The implications and future directions of intelligent robotics involve addressing both challenges and potential risks, maximizing benefits, and ensuring responsible implementation. The continuous improvement and refinement of existing technology will shape human life and industries, driving innovation and advancements in intelligent robotics.

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The Impact of Artificial intelligence and Robotics on the Future Employment Opportunities

Cited by 22 publications

References 13 publications

Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

Intelligent Robotics—A Systematic Review of Emerging Technologies and Trends

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