Cultivating a sustainable future in the artificial intelligence era: A comprehensive assessment of greenhouse gas emissions and removals in agriculture.

Agriculture is a leading sector in international initiatives to mitigate climate change and promote sustainability. This article exhaustively examines the removals and emissions of greenhouse gases (GHGs) in the agriculture industry. It also investigates an extensive range of GHG sources, including rice cultivation, enteric fermentation in livestock, and synthetic fertilisers and manure management. This research reveals the complex array of obstacles that are faced in the pursuit of reducing emissions and also investigates novel approaches to tackling them. This encompasses the implementation of monitoring systems powered by artificial intelligence, which have the capacity to fundamentally transform initiatives aimed at reducing emissions. Carbon capture technologies, another area investigated in this study, exhibit potential in further reducing GHGs. Sophisticated technologies, such as precision agriculture and the integration of renewable energy sources, can concurrently mitigate emissions and augment agricultural output. Conservation agriculture and agroforestry, among other sustainable agricultural practices, have the potential to facilitate emission reduction and enhance environmental stewardship. The paper emphasises the significance of financial incentives and policy frameworks that are conducive to the adoption of sustainable technologies and practices. This exhaustive evaluation provides a strategic plan for the agriculture industry to become more environmentally conscious and sustainable. Agriculture can significantly contribute to climate change mitigation and the promotion of a sustainable future by adopting a comprehensive approach that incorporates policy changes, technological advancements, and technological innovations.

Shirt-color recognition for the color-blindness.

Color-blind is a generic disability whereby the affected individuals are not given the opportunity to benefit from the various functions provided by color that would impact humans physically and psychologically. Although this disability is not fatal, it brought plenty of turbulence in the affected individuals' daily activities. This paper aims to develop a system for recognizing and detecting colors of clothes in images, improve accuracy by using advanced algorithms to handle lighting variations, and provide color matching recommendations to assist color-blind individuals in making informed choices when purchasing shirts. The proposed methodology for color recognition involves:•retrieving the RGB values of a given point from the input image and converting them into HSV values.•creating web application integrated with a machine learning model to classify and predict the corresponding color based on the HSV values.•predicting the color name with suggestions of matching colors will be displayed on the interface.

Post-quantum healthcare: A roadmap for cybersecurity resilience in medical data.

As healthcare systems transition into an era dominated by quantum technologies, the need to fortify cybersecurity measures to protect sensitive medical data becomes increasingly imperative. This paper navigates the intricate landscape of post-quantum cryptographic approaches and emerging threats specific to the healthcare sector. Delving into encryption protocols such as lattice-based, code-based, hash-based, and multivariate polynomial cryptography, the paper addresses challenges in adoption and compatibility within healthcare systems. The exploration of potential threats posed by quantum attacks and vulnerabilities in existing encryption standards underscores the urgency of a change in basic assumptions in healthcare data security. The paper provides a detailed roadmap for implementing post-quantum cybersecurity solutions, considering the unique challenges faced by healthcare organizations, including integration issues, budget constraints, and the need for specialized training. Finally, the abstract concludes with an emphasis on the importance of timely adoption of post-quantum strategies to ensure the resilience of healthcare data in the face of evolving threats. This roadmap not only offers practical insights into securing medical data but also serves as a guide for future directions in the dynamic landscape of post-quantum healthcare cybersecurity.

Neural matrix factorization++ based recommendation system.

In recent years, Recommender System (RS) research work has covered a wide variety of Artificial Intelligence techniques, ranging from traditional Matrix Factorization (MF) to complex Deep Neural Networks (DNN). Traditional Collaborative Filtering (CF) recommendation methods such as MF, have limited learning capabilities as it only considers the linear combination between user and item vectors. For learning non-linear relationships, methods like Neural Collaborative Filtering (NCF) incorporate DNN into CF methods. Though, CF methods still suffer from cold start and data sparsity. This paper proposes an improved hybrid-based RS, namely Neural Matrix Factorization++ (NeuMF++), for effectively learning user and item features to improve recommendation accuracy and alleviate cold start and data sparsity. NeuMF++ is proposed by incorporating effective latent representation into NeuMF via Stacked Denoising Autoencoders (SDAE). NeuMF++ can also be seen as the fusion of GMF++ and MLP++. NeuMF is an NCF framework which associates with GMF (Generalized Matrix Factorization) and MLP (Multilayer Perceptrons). NeuMF achieves state-of-the-art results due to the integration of GMF linearity and MLP non-linearity. Concurrently, incorporating latent representations has shown tremendous improvement in GMF and MLP, which result in GMF++ and MLP++. Latent representation obtained through the SDAEs' latent space allows NeuMF++ to effectively learn user and item features, significantly enhancing its learning capability. However, sharing feature extractions among GMF++ and MLP++ in NeuMF++ might hinder its performance. Hence, allowing GMF++ and MLP++ to learn separate features provides more flexibility and greatly improves its performance. Experiments performed on a real-world dataset have demonstrated that NeuMF++ achieves an outstanding result of a test root-mean-square error of 0.8681. In future work, we can extend NeuMF++ by introducing other auxiliary information like text or images. Different neural network building blocks can also be integrated into NeuMF++ to form a more robust recommendation model.