As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed.

Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.

An enhanced scatter search (eSS) with combined opposition-based learning algorithm is proposed to solve large-scale parameter estimation in kinetic models of biochemical systems. The proposed algorithm is an extension of eSS with three important improvements in terms of: reference set (RefSet) formation, RefSet combination, and RefSet intensification. Due to the difficulty in estimating kinetic parameter values in the presence of noise and large number of parameters (high-dimension), the aforementioned eSS mechanisms have been improved using combination of quasi-opposition and quasi-reflection, which were under the family of opposition-based learning scheme. The proposed algorithm is tested using one set of benchmark function each from large-scale global optimization (LSGO) problem as well as parameter estimation problem. The LSGO problem consists of 11 functions with 1000 dimensions. For parameter estimation, around 116 kinetic parameters in Chinese hamster ovary (CHO) cells and central carbon metabolism of E. coli are estimated. The results revealed that the proposed algorithm is superior to eSS and other competitive algorithms in terms of its efficiency in minimizing objective function value and having faster convergence rate. The proposed algorithm also required lower computational resources, especially number of function evaluations performed and computation time. In addition, the estimated kinetic parameter values obtained from the proposed algorithm produced the best fit to a set of experimental data.

This study presents a hybrid optimization technique to optimize stator and rotor slots of induction motor (IM) design for electric vehicle (EV) applications. The existing meta-heuristic optimization techniques for the IM design, such as genetic algorithm (GA) and particle swarm optimization (PSO), suffer premature convergence, exploration and exploitation imbalance, and computational burden. Therefore, this study proposes a new hybrid optimization technique called opposition-based jellyfish search optimization (OBJSO). This technique adopts opposition-based learning (OBL) into a jellyfish search optimization (JSO). Apart from that, a multi-objective formulation is derived to maximize the main performance indicators of EVs, including efficiency, breakdown torque, and power factor. The proposed OBJSO is used to solve the optimal design of stator and rotor slots based on the formulated multi-objective. The performance is compared with conventional optimization techniques, such as GA, PSO, and JSO. OBJSO outperforms three other optimization techniques in terms of average fitness by 2.2% (GA), 1.3% (PSO), and 0.17% (JSO). Furthermore, the convergence rate of OBJSO is improved tremendously, where up to 13.6% reduction in average can be achieved compared with JSO. In conclusion, the proposed technique can be used to help engineers in the automotive industry design a high-performance IM for EVs as an alternative to the existing motor.