Agriculture stands as the essential foundation of human sustenance, confronting the dual challenge of providing for a burgeoning global populace while safeguarding the integrity of the natural environment. This comprehensive review paper undertakes an exhaustive exploration of the continually evolving sphere of agricultural sustainability, traversing the multifaceted terrain of present-day trends, technological innovations, and the promising trajectories that lie ahead. From the vantage point of precision agriculture and climate-smart methodologies to the strategic integration of deep learning technologies, it offers a comprehensive examination of pioneering approaches that are redefining the agricultural domain. Within, it elucidates the intrinsic relationship between agriculture and sustainability, exemplifying how judicious resource management, the preservation of biodiversity, and the implementation of circular agricultural practices herald an epoch of conscientious agrarian practices. Moreover, this study casts an illuminative gaze toward the future of agriculture, wherein quantum intelligence, meta-learning, deep reinforcement learning, curriculum learning, intelligent nanothings, blockchain technology, and CRISPR gene editing converge to furnish innovative solutions. These solutions aspire to optimize crop yields, mitigate ecological footprint, and fortify global food security. As this academic voyage commences, it is incumbent to reiterate the pivotal assertion that sustainability in agriculture is not merely a desideratum; it is a compelling mandate, and the seeds of transformative innovation have been sown to recalibrate the world's approach to food production and environmental stewardship.

The proliferation of the smart and sensing devices in the field of communicating networks support in to develop the so-called Internet of Things (IoT). IoT considers a new paradigm for evolutionary of internet connectivity. IoT refers to connect objects around the real world with the Internet to accomplish the common goals and monitor these objects via wire/wireless communications. It plays a large and important role in human life through its use in many applications of human interest. Through using a variety of enabling wireless technologies as Wireless Sensor Networks (WSN), Radio Frequency Identification (RFID), Near Filed Communication (NFC), and barcode in the applications. These technologies will support IoT to transform the internet into a fully integrated future internet. This paper attempts to provide a comprehensive survey of the available literature related to IoT technologies and its applications in many areas of modern-day living. Identify the trend and directions of future research in IoT applications, depend on a comprehensive literature review and the discussion of the achievements of the researchers.

The state of the weather became a point of attraction for researchers in recent days. It control in many fields as agriculture, the country determines the types of crops depend on state of the atmosphere. It is therefore important to know the weather in the coming days to take precautions. Forecasting the weather in future especially rainfall won the attention of many researchers, to prevent flooding and other risks arising from rainfall. This Paper presents a vigorous hybrid technique was applied to forecast rainfall by combining Particle Swarm Optimization (PSO) and Multi-Layer Perceptron (MLP) which is popular kind used in Feed Forward Neural Network (FFNN). The purpose of using PSO with MLP is not just to forecast the rainfall but, to improve the performance of the network; this was proved by comparison with various Back Propagation (BP) an algorithm such as Levenberg-Marquardt (LM) through results of Root Mean Square Error (RMSE). RMSE for MLP based PSO is 0.14 while RMSE for MLP based LM is 0.18.

Most studies in recent decades focused on transforming linear economics into circular through recovering and remanufacturing the products. Circular Economies (CE) aim to minimize the usage of resources by utilizing the waste in production as new or raw materials. Interconnectivity between parties in the industrial system provides decision-makers with rich information and anticipation of failure. Industry 4.0 technologies (I4.0) allow for handling such issues, protecting the environment by utilizing resources efficiently, and restructuring the industry to be smarter as well. This paper contributes to achieving cleaner production (CP), CE, and social for manufacturers through the linkage between 6R methodology with new technologies of I4.0 such as Blockchain technology (BCT) and big data analytical technology (BDA). In this paper, the authors proposed a Multi-criteria decision-making (MCDM) decision framework based on the best-worst method (BWM), Decision-Making trial and evaluation laboratory (DEMATEL), Technique for order of preference by similarity to ideal solution (TOPSIS), and Complex Proportional Assessment (COPRAS). The authors contributed to addressing the weaknesses and problems of these subjective MCDM methods through the cooperation of the neutrosophic theory with the usage of MCDM methods in this work. In the first stage, all criteria that influence sustainable manufacturer selection are specified using literature research on this topic. BWM-based neutrosophic theory was combined to get the criteria’s weights with the aid of DEMATEL-based neutrosophic to obtain the least and best criteria used in BWM in the second stage. The optimal sustainable manufacturer was selected based on TOPSIS and COPRAS under neutrosophic theory in the third and fourth stages, respectively. Furthermore, a case study performed indicated manufacturer 2 (A₂) is an optimal sustainable manufacturer in two ranking methods otherwise, manufacturer 4 (A₄) is the worst sustainable manufacturer. The contribution of this work is to propose a hybrid MCDM with an uncertainty theory of neutrosophic for sustainable manufacturer selection based BDA-BCT with 6R. Sensitivity analyses were carried out to show the decision’s flexibility in various scenarios. Finally, the consequences for management viewpoints were considered.

The advent of deep learning has revolutionized the landscape of organizational decision-making by offering powerful tools for data analysis and prediction. In this comprehensive survey, we explore the intersection of deep learning and organizational decision-making, elucidating the theoretical underpinnings, empirical evidence, and practical implications of this synergy. Theoretical foundations and research hypotheses are rigorously examined, providing a solid framework for understanding the role of deep learning models in enhancing decision-making processes. We delve into the systematic survey, which encompasses a wide spectrum of applications across various industries and domains, showcasing how deep learning empowers decision support systems, augments data-driven decision-making, and refines decision-making frameworks. Drawing inspiration from the Egyptian Vision 2030, we explore the implications of deep learning-based decision-making on national development strategies and policy implementation. Our analysis sheds light on the transformative potential of these technologies, offering insights into how organizations, particularly in Egypt, can harness these advancements to achieve their developmental goals. Finally, we outline future directions in this field, highlighting emerging trends, technological advancements, and potential areas for further research. As the digital age continues to reshape the landscape of decision-making, this survey serves as a valuable resource for researchers, policymakers, and practitioners seeking to leverage deep learning for empowered, data-driven, and informed organizational decisions.

Industry 4.0 provides businesses with the tools they need to meet difficulties such as fluctuating demand and unstable markets. Additionally, Industry 4.0 refers to the connectivity of computers, various materials, and artificial intelligence (AI) with minimum involvement from humans in the decision-making process. Although Industry 4.0 has a significant potential for the expansion of the industrial sector, it faces several hurdles, including integration of technology, problems with human resources, problems with supply chains, and data security concerns. The human-centered approach that Industry 5.0 took meant that many of the problems that plagued Industry 4.0 could finally be solved. In the previous generation, known as Industry 4.0, the emphasis was placed on scalability and volume of production; however, in the next generation, known as Industry 5.0, human centricity is the key focus. We have included a list of the different technical improvements that are part of Industry 5.0 as well as the technological advancements that are part of Industry 4.0. The problems that plagued Industry 4.0 have been addressed head-on in Industry 5.0, including concerns over data protection and the integration of new technologies. This study would serve as a foundation for academics and companies to learn about the technologies of Industry 4.0, their obstacles, the technical advancements, and the methods by which Industry 5.0 addressed the issues of Industry 4.0. Also, we constructed an appraiser model to appraise manufacturers as partners in the supply chain. We selected manufacturers that are interested in deploying Industry 5.0 in their operations. Analytic Hierarchy Process (AHP) and Complex Proportional Assessment (COPRAS) are contributed to construct an appraiser model under the authority of uncertainty theory entailed in single value neutrosophic sets (SVNSs) to support AHP and COPRAS in ambiguity situations.