More Like this

1. Applications of artificial intelligence technologies in water environments: From basic techniques to novel tiny machine learning systems

   https://doi.org/10.1016/j.psep.2023.09.072  

   Bagheri, Majid; Farshforoush, Nakisa; Bagheri, Karim; Shemirani, Ali Irani (September 2023, Process Safety and Environmental Protection)

   Chen, Guohua; Khan, Faisal (Ed.)

   Artificial intelligence (AI) and machine learning (ML) are novel techniques to detect hidden patterns in environmental data. Despite their capabilities, these novel technologies have not been seriously used for real-world problems, such as real-time environmental monitoring. This survey established a framework to advance the novel applications of AI and ML techniques such as Tiny Machine Learning (TinyML) in water environments. The survey covered deep learning models and their advantages over classical ML models. The deep learning algorithms are the heart of TinyML models and are of paramount importance for practical uses in water environments. This survey highlighted the capabilities and discussed the possible applications of the TinyML models in water environments. This study indicated that the TinyML models on microcontrollers are useful for a number of cutting-edge problems in water environments, especially for monitoring purposes. The TinyML models on microcontrollers allow for in situ real-time environmental monitoring without transferring data to the cloud. It is concluded that monitoring systems based on TinyML models offer cheap tools to autonomously track pollutants in water and can replace traditional monitoring methods. 

   more » « less
2. Smart Agent System for Cyber Nano-Manufacturing in Industry 4.0

   https://doi.org/10.3390/app12126143  

   Almakayeel, Naif; Desai, Salil; Alghamdi, Saleh; Qureshi, Mohamed Rafik (June 2022, Applied Sciences)

   The development of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) has influenced Cyber-Physical Manufacturing Systems (CPMS). Collaborative manufacturing among organizations with geographically distributed operations using Nanomanufacturing (NM) requires integrated networking for enhanced productivity. The present research provides a unique cyber nanomanufacturing framework by combining digital design with various artificial neural networks (ANN) approaches to predict the optimal nano/micro-manufacturing process. It enables the visualization tool for real-time allocation of nano/micro-manufacturing resources to simulate machine availability for five types of NM processes in real-time for a dynamic machine identification system. This research establishes a foundation for a smart agent system with predictive capabilities for cyber nanomanufacturing in real-time. 

   more » « less
3. An Assure AI Bot (AAAI bot)

   https://doi.org/10.1109/ISNCC55209.2022.9851759  

   Tellez, N; Serra, J; Kumar, Y; Li, J; Morreale, P (July 2022, Proceedings of ISNCC)

   Artificial Intelligence (AI) bots receive much attention and usage in industry manufacturing and even store cashier applications. Our research is to train AI bots to be software engineering assistants, specifically to detect biases and errors inside AI software applications. An example application is an AI machine learning system that sorts and classifies people according to various attributes, such as the algorithms involved in criminal sentencing, hiring, and admission practices. Biases, unfair decisions, and flaws in terms of the equity, diversity, and justice presence, in such systems could have severe consequences. As a Hispanic-Serving Institution, we are concerned about underrepresented groups and devoted an extended amount of our time to implementing “An Assure AI” (AAAI) Bot to detect biases and errors in AI applications. Our state-of-the-art AI Bot was developed based on our previous accumulated research in AI and Deep Learning (DL). The key differentiator is that we are taking a unique approach: instead of cleaning the input data, filtering it out and minimizing its biases, we trained our deep Neural Networks (NN) to detect and mitigate biases of existing AI models. The backend of our bot uses the Detection Transformer (DETR) framework, developed by Facebook, 

   more » « less
4. How Artificial Intelligence and Deep Learning are Changing the Healthcare Industry

   Thompson, Stephen (January 2021, The ADMI 2021 Symposium)

   null (Ed.)

   This research paper will discuss the various technological innovations that have been developed within the past, are being developed in the present and will be developed in the future to bring progressive change within the healthcare industry. In particular, the way artificial intelligence and deep learning has brought about new methods for making task more efficient, less time consuming, and possibly more accurate. This study will discuss the different positive and negative effects of these technological methods to best see how artificial intelligence (AI) and deep learning is impacting the health industry in all areas. With supportive interviews from medical professionals, data scientist, and machine learning researchers, the analysis of real-world experiences and scenarios will provide more insight to what is beneficial or ineffective. The study utilizes extensive literary works to examine in detail the current AI and deep learning new approach to medicine, the risk and ethical concerns, the technological challenges from determining the best algorithms, and myths versus reality surrounded by this subject. 

   more » « less
5. Malware Detection and Prevention using Artificial Intelligence Techniques

   https://doi.org/10.1109/BigData52589.2021.9671434  

   Hossain Faruk, Md Jobair; Shahriar, Hossain; Valero, Maria; Barsha, Farhat Lamia; Sobhan, Shahriar; Khan, Md Abdullah; Whitman, Michael; Cuzzocrea, Alfredo; Lo, Dan; Rahman, Akond; et al (January 2022, 2021 IEEE International Conference on Big Data (Big Data))

   With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholder’s, particularly, end user’s security, protecting the data from fraudulent efforts is one of the most pressing concerns. A set of malicious programming code, scripts, active content, or intrusive software that is designed to destroy intended computer systems and programs or mobile and web applications is referred to as malware. According to a study, naive users are unable to distinguish between malicious and benign applications. Thus, computer systems and mobile applications should be designed to detect malicious activities towards protecting the stakeholders. A number of algorithms are available to detect malware activities by utilizing novel concepts including Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we emphasize Artificial Intelligence (AI) based techniques for detecting and preventing malware activity. We present a detailed review of current malware detection technologies, their shortcomings, and ways to improve efficiency. Our study shows that adopting futuristic approaches for the development of malware detection applications shall provide significant advantages. The comprehension of this synthesis shall help researchers for further research on malware detection and prevention using AI. 

   more » « less