Deaf and hard-of-hearing persons communicate with each other and their communities by using sign language. As this is a natural method for people to interact with computers, many academics are researching it in order to make it simpler and more natural without the use of additional gadgets. So, the main objective of gesture recognition research is to develop systems that can recognise certain human gestures and use them, for instance, to communicate information. Quick, extremely accurate hand detection and real-time gesture identification must be possible with vision-based hand gesture interfaces. Learning sign movements is the first step in creating words and sentences for computer-assisted sign language interpretation. Both static and dynamic sign actions are available. Both types of gesture recognition are crucial to human culture, even if static gesture recognition is easier than dynamic gesture recognition. This paper outlines the processes needed to recognise sign language. The method of gathering data, as well as its preprocessing, transformation, feature extraction, categorization, and outcomes, are all examined. For this area of study, there are also some suggested directions for further research. The visual-manual modality used by sign languages is a collection of established languages. When a hand motion is inputted, the system instantly displays the appropriate recognized character on the monitor screen. In the following, research projects that led to a system that uses convolutional neural networks to identify handwriting on the basis of depth pictures the camera collects.
Some systems correct faulty production to reduce the cost and increase the performance of steel plates. The first element of quality production is to identify and classify defects. However, when the error classification is not done well, the cost of correcting the error increases. This study proposes a classification and regression tree (CART) based fuzzy model to determine which fault class the faulty steel plates fall into. The second model used in the study is the adaptive neuro-fuzzy interface system (ANFIS). In both approaches, error classes were determined using fuzzy logic. In the two models created, it was observed that when the information of the detected faulty steel plate was entered, it was unsuccessful in determining which class this fault belonged to. Although it suggests a quick solution for detecting the error class, it has been seen that these approaches are not appropriate to use because they do not offer the right solution.
HVAC systems are heating, ventilation, and air conditioning units that allow for maintaining indoor air conditions to a desired degree. Other than maintaining occupant comfort, its use in industrial applications and critical infrastructure is vital to maintain operations since many systems and components must operate under controlled environments, such as, to avoid overheating. HVAC systems by design do not incorporate security. Given this, most HVAC systems , especially those connected to the internet are vulnerable to cyber attacks from various angles. Such an example was the Stuxnet virus which sabotaged a nuclear plant in 2010. Due to this and among other events, HVAC security is now under the spotlight, and the need for a solid security model for HVAC security is necessary. In this paper, we present an anomaly-based detection machine learning model to detect malicious sabotage of HVAC systems which achieves an accuracy of 99% and recall of 98.2%.
It is of paramount importance that software applications stay performant as the number of users increases due to certain predictable events such as sales, promotional campaigns and seasons. Modeling, typically using Queuing Networks, is commonly used to predict the application’s performance characteristics to help in finding the extra required computing resources.Building Queueing Network Models to represent the various hardware and software components is a complex and ad-hoc task due to the lack of generalized models. Additionally, obtaining model parameters, known as Service Demands, is a time consuming inaccurate process. Furthermore, solving such complex models requires the use of slow and resource consuming simulation techniques. Simplifying models and solutions to rectify these issues results of inaccurate predictions leading to wasted computing resources.In this work, we propose the use of Deep Neural Networks to build software application models to predict performance characteristics under varying workloads. Unlike Queuing Networks, Deep Neural Networks are built using well-established generalized approaches. Additionally, their input data can be obtained from the applications performance monitoring data or using typical performance measuring techniques. Finally, solving Deep Neural Networks is way more efficient than the simulation techniques of Queuing Networks.
As the world becomes increasingly digitized, the use of artificial intelligence (AI) has the potential to revolutionize sustainable computing practices. In this paper, we provide a comprehensive review of the impact of AI on sustainable computing, including its applications, case studies, challenges, and limitations. Our results demonstrate that AI can significantly reduce the environmental impact of computing systems and enable more sustainable practices, such as energy-efficient data centers, smart grids, and precision agriculture. However, the adoption of AI in sustainable computing also poses ethical and social challenges that must be addressed, such as bias, privacy, and job displacement. To address these challenges, we propose a governance framework for ethical and socially responsible AI in sustainable computing. We conclude by highlighting the contributions of this paper, including a comprehensive review of the literature and a framework for ethical and socially responsible AI. We also provide future research directions for the field of AI and sustainable computing, such as designing AI-powered smart cities and exploring the role of AI in the circular economy.
