Rice (Oryza sativa) is Indias major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy.

Higher education is essential because it exposes students to a variety of areas. The academic performance of IT students is crucial and might fail if it isn’t documented to identify the features influencing them, as well as their strengths and shortcomings. The student academic prediction system needs to be enhanced so that teachers can forecast their students’ performance. Numerous studies have been conducted to increase the prediction accuracy of IT students, but they encountered difficulties with unbalanced data and algorithm tuning. To address these issues, the study proposed different machine learning (ML) algorithms that handled imbalanced data by applying the synthetic minority oversampling technique (SMOTE) and employing hyperparameter tuning algorithms to enhance prediction during the training process. The ML models we used were decision tree (DT), k-nearest neighbor, and XGBoost. The models were fine-tuned by applying Ant colony optimization (ACO) and artificial bee colony optimization techniques. Subsequently, these optimization techniques further enhanced the performance of the models. After comparing them, the results showed that SMOTE and ACO combined with the DT model outperformed other models for academic prediction. Additionally, the study utilized the Kendall Tau correlation coefficient technique to analyze the correlation between features and identify factors that positively or negatively impact student success.

In this paper, RF MEMS switch with capacitive contact is designed and analyzed for Ka band application. A fixed‐fixed beam/meander configuration has been used to design the switch for frequency band 10 GHz to 40 GHz. Electromagnetic and electromechanical analysis of three‐dimensional (3D) structure/design has been analyzed in multiple finite element method (FEM) based full‐wave simulator (Coventorware and high‐frequency structure simulator). A comparative study has also been carried out in this work. The high resistivity silicon substrate (tanδ = 0.010, ρ > 8 kΩ − cm, ε_r = 11.8) with a thickness of 675 ± 25 μm has been taken for switch realization. The designed structure shows an actuation voltage of around 9.2 V. Impedance matching for the switch structure is well below 20 dB, loss in upstate, i.e., insertion loss >0.5 dB, and isolation of >25 dB throughout the frequency band is observed for the aforesaid structure. Furthermore, to increase the RF parameters, AIN dielectric material has been used instead of SiO₂ resulting in capacitance in downstate that increases hence improved the isolation. The proposed switch can be utilized in various potential applications such as any switching/tunable networks phased‐array radar, reconfigurable antenna, RF phase shifter, mixer, biomedical, filter, and any transmitter/receiver (T/R) modules.

The pandemic outbreak has revealed significant flaws in the complex and highly fragmented Healthcare Supply Chain’s (HSC’s). However, two major issues persist in the HSCs, leading to inefficiencies: transparency in vaccine distribution and accuracy in demand forecasting. The recent pandemic has highlighted and intensified existing vulnerabilities in HSC’s, leading to the effective utilization of digital technologies to manage them. This research proposes a novel framework that merges Blockchain (BC) and Machine Learning (ML) to bolster the HSCs amidst pandemics, by developing a framework named the Predictive BlockVax Distribution Network (PBDN) model. The proposed PBDN model utilizes BC for securing transactions and Long Short-Term Memory (LSTM), for precise demand prediction. Leveraging Hyperledger Besu, which represents an Ethereum client that is accessible for public use, the PBDN framework ensures BC’s privacy, scalability, and efficient network operations, while LSTM’s advanced forecasting outperforms traditional models and Deep Learning (DL) techniques. This integration showcases a significant leap in managing vaccine distribution and enhancing system resilience, fairness, and transparency. The proposed PBDN model illustrates the potential of BC and ML together to tackle pandemic-induced Supply Chains (SC’s) disruptions, providing a decentralized solution that supports autonomous, informed decision-making without third-party dependency. This approach not only addresses immediate challenges but also sets a precedent for future crisis response, emphasizing the need for robust, Transparent Supply Chain’s (TSC’s).

Several studies have shown that Artificial Intelligence (AI) algorithms can accurately predict crop yields based on data from various sources, such as weather conditions, soil moisture levels, and crop growth rates. Integration of AI in agriculture has the potential to revolutionize the way we grow and produce food. By collecting and analysing data, optimizing resource utilization, and increasing sustainability, AI can help farmers improve crop yields and reduce waste, leading to a more efficient and sustainable agricultural sector. However, there are also concerns about the impact of AI on employment and the potential for unintended consequences, so it will be important to proceed with caution and continue to monitor the effects of AI on agriculture. The use of (AI) in agriculture has been the subject of numerous studies and research papers in recent years. In this chapter, authors examine some of the key findings and insights from the agriculture sector. Major contributions by a number of start-ups have been considered for explanation purpose.