White Blood Cells are important in determining a person's overall health. The blood disease diagnosis includes characterization and identification of blood samples of a patient. Neural Networks (NN), Convolutional Neural Networks (CNN), and a mix of CNN and NN models are used in recent techniques to improve visual content understanding. From start to finish, The authors were driven to uncover remarkable characteristics in example photographs because of their expertise in medical image analysis. For blood cell classification, the overall performance of individual cell patches extracted using blood smear techniques has been excellent. These approaches, on the other hand, are incapable of dealing with the issue of multiple cells overlapping. Because of the blood cell overlapping pictures, the input image dimension is compressed, the classification time is reduced, as well as the network works better with more accurate parameter estimates. In this review, we are evaluating a detailed scientific comparison of some of the ways used to improve WBC classification. The authors will show some of the ways used to automatically classify their cells. The results of some of the tests used using available data, compared to blood cell classification techniques.

Drones are widely used nowadays in different sectors such as military, agriculture, surveillance, smart city measurements, logistics, and disaster rescue. In particular, multi-drone systems are gaining wider use for various problems, especially those requiring real-time navigation, simultaneous coordination, or collaboration between drones to attain various mission goals. One of the fundamental challenges for such systems is Path Planning, which requires computing an optimal collision-free trajectory from a source to a destination position in the presence of obstacles and moving agents. A wide range of algorithms, introducing nature-inspired metaheuristic methods like PSO, WOA, and LSO, have been developed to cope with this challenge. Reinforcement learning has also allowed drones to learn paths through environmental interaction and trial and error. Despite the strengths of those algorithms, they suffer from drawbacks, including computational complexity, scalability issues, being trapped in local minima, premature convergence, etc. This Review highlights the merits and demerits of existing Path Planning algorithms. It notes that while these algorithms have made considerable progress, they still suffer from limitations and do not yet account for the fractal and stochastic properties that appear in multi-drone systems. Fractal properties ensure solutions can adapt but remain valid and scalable throughout a hierarchy of environments. At the same time, stochastic methods enhance global exploration by weaving in a level of memory-less, random exploration of solutions. For future work, incorporating these features in the upcoming algorithms will make multi-drone performance even more robust, efficient, and adaptable and encourage their successful use in the real world.

Quantum-dot cellular automata (QCA) are one of the most promising alternatives to traditional VLSI technology despite significant current obstacles. The QCA has the advantages of very low power dissipation, faster switching speed, and extremely low circuit area, which can be used in designing nano-scale image processing circuits. Morphological operations and processing of digital image processing is a significant topic for researchers because it is widely used for analyzing, enhancing, and modifying images to extract meaningful information or improve their visual quality. Image processing is also used for image retrieval and enhancement, image compression, object recognition, machine vision, and medical applications. QCA technology, as a new and leading technology with great potential, can play a fundamental role in morphological operations, processing digital images, image editing, medical imaging, facial recognition, and autonomous vehicles. In recent years, researchers in this field have presented many circuits, but they have many flaws in terms of speed, accuracy, and area consumption, and the need to create more efficient circuits is felt more than ever. Therefore, in this article, a new design for morphological operations and processing digital images is presented using QCA technology. This paper presents a new efficient QCA-based implementation of image processing based on the direct interactions between the QCA cells. This circuit uses two majority gates of five new inputs to produce the output and produces the desired output. In addition, a comparison and analysis of the area and clocking complexity, design cost, and energy dissipation through simulation using QCADesigner and QCADesigner-E are done. The results show that the presented circuit produces the expected and correct output results in 0.75 clock phases, and the obtained results show the high speed and low consumption space of the presented circuit. In addition, the presented circuit performs better than the previous best circuits in terms of quantum cost and delay, and according to the mentioned advantages, it can be used to improve and expand other circuits in image processing.