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AI powered smart farming transform agriculture landscape. This article based on statistical data that looks into the future of AI with respect to agriculture business. This article creates a path to shed insights on the significant impact of artificial-intelligence in terms of shaping up and automating agricultural operations that would let the reader focus their visualization on root-level processes within food production business. This study is based on the analysis of market data as well as on research results to identify how artificial intelligence is used, the key benefits and perspectives in smart farming. Computer vision, data analytics and machine learning are among the applications of these technologies. The study found out that AI has really Improved Efficiency. The outcomes as per their study show that via automation, one may succeed in bringing the labor cost to 50%, agriculture yield will be increased by 15%, and can reduce use of irrigation water even low down to 20%, having an average increase in the productivity change up to a mark value around at least 30%. This deserves a chapter of its very own, Intelligent irrigation systems and fertilization choices and pest control protocols driven by data. The system is able to identify plant diseases, monitor animal health, and optimize cattle feed, and these features can be expected to increase the productivity of agriculture Although the data obviously reflects challenges such as misuse of data, limited entry for small actors and ongoing technological and infrastructure development. The clear finding is that AI has the potential to dramatically transform food production across primary agriculture. Which can lead to higher yields, increased profitability, sustainable practices and one: of the most powerful tools in addressing global food security. We need to adapt this innovative technology and its ethical and equitable implementation to ensure the future sustainability of agriculture.

Background: The fast growth of the Internet of Things (IoT) in Fifth-Generation (5G) networks has emphasised the need to examine energy efficiency thoroughly. Ensuring the sustainable implementation of IoT devices in these sophisticated networks is paramount. Objective: The article seeks to experimentally examine the disparities in energy use between two commonly utilised microcontroller platforms, namely the Arduino Nano and Arduino Micro when included in 5G network settings. Methodology: The power consumption of the Arduino Nano and Arduino Micro was evaluated by connecting them to 5G connectivity modules. The assessment centered on three operating states: data acquisition, transfer, and idle. Power measurements were performed using calibrated instruments in a controlled experimental environment to achieve precise and consistent results. Results: The findings indicated that the Arduino Micro, while carrying out comparable computing and data transmission operations in a 5G setting, spent around 8% less power than the Arduino Nano. The difference in energy consumption may be primarily due to the Arduino Micro's superior voltage control technologies and optimized low-power components. In contrast, the Arduino Nano demonstrated reduced power consumption in standby mode, indicating an intricate and non-linear correlation between operational states and power utilisation. Conclusion: The results of this study provide vital information for choosing microcontroller platforms for 5G IoT applications. The Arduino Micro is a more energy-efficient option for jobs requiring constant data processing and transmission. On the other hand, the Arduino Nano is better suited for situations where there is intermittent network access. The article contributes substantially to the current discussion on improving energy efficiency in Internet of Things (IoT) devices for 5G networks.

Background: With the escalating number of visually impaired individuals globally, the demand for effective walking aids has intensified. While various solutions have emerged, many face constraints in their applicability. Objective: This study delves into the advancements and limitations of modern walking aids designed for the visually impaired over recent years. It further seeks to establish a foundation for future research to enhance the functionality and efficiency of these aids, aiming to bolster the autonomy and safety of visually impaired individuals. Methods: A comprehensive review of current walking aids was conducted, juxtaposing their merits and drawbacks. Emphasis was placed on advancements in sensors, computer vision, and smartphone integrations to ameliorate the challenges inherent in existing solutions. Results: The study underscores a pressing need for holistic frameworks in designing walking aids tailored to the unique challenges faced by visually impaired individuals. While traditional aids like white canes and guide dogs remain prevalent, they often fall short of providing comprehensive navigational support. Conclusion: Embracing cutting-edge technologies in walking aids design can considerably enhance the visually impaired’ s independence and quality of life. This article serves as a clarion call to stimulate further investigations into walking aids, enriching the prevailing body of knowledge

Background: As information and communication systems become more sophisticated and linked, there is an increasing demand for effective information security and cybersecurity measures. The modern age necessitates a methodical approach to evaluating and fortifying these systems against emerging cyber threats. Objective: This study provides a unique technique for assessing the effectiveness of information security and cybersecurity solutions. The technique tries to discover and correct possible vulnerabilities by examining individual indications, providing enterprises with a data-centric strategy to enhance their cyber defenses. Methods: The approach is created utilizing a large dataset from many firms and industries. To illustrate critical security components, key performance indicators (KPIs) such as incident response time, threat detection rates, and vulnerability repair were chosen. Advanced statistical approaches and machine learning algorithms were used to evaluate the effectiveness of these systems against set KPI criteria. Results: The current study yields significant insights into the strengths and limitations of various security techniques. By evaluating specific indicators, opportunities for improvement were found, allowing firms to focus their security operations more effectively. The study validated the influence of system complexity and security investment on overall system efficiency. Conclusion: This article proposes a data-driven strategy to improve the efficiency of information and cybersecurity systems, providing companies with a road map for protecting their essential assets and data in the face of an ever-changing cyber threat scenario.

A system with multiple controllers and a multiple-model architecture is one of the most effective solutions for the real-time control of nonlinear systems. The employment of these structures necessitates the resolution of certain difficulties, such as selecting the optimal algorithm or switching control algorithms. Based on the concepts of auto-transfer, the paper provides a way for switching the numerous controller structures’ algorithms. This paper presents a real-time dynamic model and platform of a magnetic levitation system (Maglev). The method’s applicability was demonstrated by utilizing a real-time architecture with an RST controller mechanism and real. In conclusion, the software was implemented and demonstrated by using the LabVIEW platform in real-time, and the results reveal that this solution can stabilize the ball’s location and has strong disturbance rejection because of the multi-zone effect.