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Color contrast pertains to graphics and the field of design. Visual objects can be described nicely with the best contrast combinations used in their representation. Color contrast suggestion is usually done with color theory, which defines two colors exactly opposite or adjacent in color hue are good contrast with each other. Herein, this paper presents a Color Contrast Recommendation System (CCRS) as an innovative solution based on Artificial Neural Networks (ANN). The main aim of the paper is to facilitate different users to find suitable contrast for any base color. We used a simple neural network model with two hidden layers for a regression task. The proposed model suggests three contrast layers for the base color given by the user. We prepare a data set of 420 color combinations for training our Neural Network model that looks appealing together and enhances the visuals. The proposed color contrast recommendation application based on Neural Networks represents a significant advancement in leveraging AI technology to streamline the design process, improve accessibility, and enhance user experiences across digital platforms.

Artificial Intelligence is a popular topic these days. It is believed that artificial intelligence will be the solution to all existing societal problems, reducing human effort while also improving human interpretation of the world around them. Artificial intelligence, within the realm of computer science, is focused on developing machines capable of emulating human thought processes, operations, and adaptability. At present, artificial intelligence finds utility across diverse domains, including but not limited to medicine, engineering, agriculture, self-driving vehicles, and aviation. Another emerging arena for AI deployment is in energy platforms. This paper delves into the applications of artificial intelligence and their significance in fostering sustainability across various trends. Consequently, it provides insights and analysis regarding the longterm viability of AI, encompassing environmental and economic development. Furthermore, it highlights the positive impact of AI applications on sustainable development, encompassing aspects such as fulfilling energy requirements, generating employment opportunities, and enhancing environmental preservation.

The deep learning teaching model is a student-centred teaching method that emphasizes guiding students to deep understanding and internalization of knowledge. Compared with traditional shallow learning, it focuses on the cultivation of students' critical thinking, innovation ability and cooperation spirit. And under today's trend of deep integration of information technology and teaching, how to utilize information technology to promote deep learning of college students has become a hot topic in educational research. In this paper, we design a teaching strategy aimed at promoting educational change and innovation through modernized information technology, and take the course "Programming Fundamentals" as an example, and carry out research from the aspects of design principles, learning activities, teaching mode construction, and effect evaluation, aiming to improve the teaching effect of the course and cultivate talents with independent learning ability, teamwork ability and innovation ability.

Greenhouse cultivation plays a pivotal role in modern agriculture by providing a controlled environment for plant growth. The significance of greenhouse insulation lies in its ability to create optimal conditions for plant development, ensuring increased crop productivity and quality. This paper emphasizes the criticality of greenhouse insulation and the necessity for effective predictive models to anticipate plant growth and yield accurately. This research proposes the utilization of Machine Learning (ML)and Deep Learning (DL) techniques to forecast plant growth and yield within controlled greenhouse settings. To achieve this, a novel deep recurrent neural network (RNN) architecture employing the Long Short-Term Memory (LSTM) neuron model is deployed in the prediction process.The study offers a comparative analysis involving various ML methodologies such as support vector regression and random forest regression. The performance evaluation of these diverse methods is conducted using the mean square error criterion to assess their effectivenessin predicting plant growth and yield. The model's sophisticated architecture enables it to produce accurate and timely predictions regarding specific growth parameters by leveraging an advanced neural network. This holistic approach introduces a novel perspective in greenhouse tomato cultivation, providing growers with valuable insights to facilitate informed decision-making, streamline resource distribution, and promote heightened agricultural sustainability.

Skin cancer is the most lethal because skin cells develop abnormally. Finding skin cancer early is very important and may help stop some kinds of skin cancer, like melanoma and focal cell carcinoma. Early detection and classification of skin cancer are difficult and costly. Recurrent networks and ConvNets can automatically extract complex data. This paper proposes to use a handmade features-based multi-layer perceptron and a cascaded ensembled network to upgrade ConvNet models. This convolutional neural network model detects non-handmade picture qualities and generates features like color moments and material properties. With ensembled DL, accuracy increased from 85.3% with convolutional neural networks to 98.3%.

Efficient traffic flow prediction is paramount in modern urban transportation management, contributing significantly to energy efficiency and overall sustainability. Traditional traffic prediction models often struggle in complex urban traffic networks, especially at multi-intersection junctions. In response to this challenge, this research paper presents a pioneering approach that not only enhances traffic flow prediction accuracy but also indirectly supports energy efficiency. This study leverages deep learning techniques, specifically the Gated Recurrent Unit (GRU), to analyze traffic patterns simultaneously at multiple intersections within a city. By treating the entire traffic network as a distributed system, the model provides real-time predictions, allowing for better traffic management and reduced fuel consumption. Moreover, the incorporation of data fusion techniques, which integrate data from various sources, including traffic sensors and historical traffic information, bolsters the accuracy and robustness of predictions. By predicting traffic flows with precision, this research aids in optimizing traffic signal timing, reducing congestion, and ultimately promoting more efficient transportation systems, which, in turn, reduces fuel wastage and emissions. This study, therefore, advances intelligent transportation systems and offers a promising pathway toward improved energy efficiency in urban mobility.

The utilization of neural model techniques for predicting learner performance has exhibited success across various technical domains, including natural language processing. In recent times, researchers have progressively directed their attention towards employing these methods to contribute to socioeconomic sustainability, particularly in the context of forecasting student academic performance. Additionally, educational data frequently encompass numerous categorical variables, and the efficacy of prediction models becomes intricately tied to sustainable encoding techniques applied to manage and interpret this data. This approach aligns with the broader goal of fostering sustainable development in education, emphasizing responsible and equitable practices in leveraging advanced technologies for enhanced learning outcomes. Building on this insight, this paper presents a literature review that delves into the use of machine learning techniques for predicting learner outcomes in online training courses. The objective is to offer a summary of the most recent models designed for forecasting student performance, categorical coding methodologies, and the datasets employed. The research conducts experiments to assess the suggested models both against each other and in comparison to certain prediction techniques utilizing alternative machine learning algorithms concurrently. The findings suggest that employing the encoding technique for transforming categorical data enhances the effectiveness of deep learning architectures. Notably, when integrated with long short-term memory networks, this strategy yields exceptional results for the examined issue.