Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

-for cell design, path loss is a very important issue and has been studied for a long time. For the new generation of mobile networks, innovative prediction models with extended frequencies are needed. The goal of this paper is to analyze six different path loss prediction models: free space, extended COST-231 Hata, empirical Hata, Walfisch-Ikegami, Stanford University Interim (SUI) and Ericson (9999). The article shows that SUI, Ericsson and Empirical Hata are overall the best choice for the new generation of mobile networks regardless of distance and type of environment. However, SUI outperforms Ericsson and Empirical Hata for 3.5 GHz for both urban and suburban environments. For higher frequencies (28 GHz), which is needed for the new generation of mobile networks like 5G, it is shown that Ericsson model gives better results for path loss in urban compared with SUI which gives better results in suburban environment. This conclusion is confirmed by introducing the average of path loss.

<span>This article introduces a full-rate differential distributed orthogonal space-time coding technique using the amplify-and-forward protocol. The proposed technique has a markedly low encoding and decoding complexity at all transmitting and receiving terminals. Furthermore, the method does not need either differential encoding or channel state information at any transmitting or receiving terminal where the information symbols are directly transmitted. Instead, the differential detection scheme is performed at the destination terminal. In our simulations, the performance of the suggested technique is performed by computer simulations in Rayleigh fading channel, using the amplify-and-forward protocol, to show that our proposed differential technique outperforms the corresponding reference techniques</span>

Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform modern agriculture by increasing crop yields while minimizing waste. Fifteen different algorithms have been considered to evaluate the most appropriate algorithms to use in agriculture, and a new feature combination scheme-enhanced algorithm is presented. The results show that we can achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99.46% using Naïve Bayes Classifier and Hoeffding Tree algorithms. These results will indicate an increase in production rates and reduce the effective cost for the farms, leading to more resilient infrastructure and sustainable environments. Moreover, the findings we obtained in this study can also help future farmers detect diseases early, increase crop production efficiency, and reduce prices when the world is experiencing food shortages.

No abstract