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Chromosome images are commonly used in karyotype analysis to diagnose chromosomal diseases. However, there are often chromosome adhesion and overlaps in chromosome images, so effective chromosome segmentation is conducive to smooth karyotype analysis. To date, some progress has been made in automatic chromosome segmentation, and existing methods can be used to segment overlapping chromosomes in most cases. However, when two or more overlapping regions are too close to each other in the image of overlapping chromosomes, the existing segmentation methods adjust the non-overlapping regions that do not belong to the overlapping region, resulting in incomplete segmentation of chromatids. Therefore, we use a heuristic algorithm to solve this problem from the point of view of mathematics and geometry to improve the segmentation of overlapping chromosomes. Starting from chromosome images, the existing problems and solutions are explained and displayed in the way of visualized interpretable image features, which helps to better understand the algorithm. Our method achieves 92.86% splicing accuracy and 90.44% overall segmentation accuracy on open datasets. The experimental results show that our method can effectively improve the problem of incorrect chromosome segmentation when two or more overlapping parts of overlapping chromosomes are too close to each other. It can accelerate the development of artificial intelligence in computational pathology and provide patients with more accurate medical services. [ABSTRACT FROM AUTHOR]

This paper introduces a novel and complete framework for solving different Internet of Things (IoT) applications, which explores eXplainable AI (XAI), deep learning, and evolutionary computation. The IoT data coming from different sensors is first converted into an image database using the Gamian angular field. The images are trained using VGG16, where XAI technology and hyper-parameter optimization are introduced. Thus, analyzing the impact of the different input values in the output and understanding the different weights of a deep learning model used in the learning process helps us to increase interpretation of the overall process of IoT systems. Extensive testing was conducted to demonstrate the performance of our developed model on two separate IoT datasets. Results show the efficiency of the proposed approach compared to the baseline approaches in terms of both runtime and accuracy. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures are highly susceptible to many security vulnerabilities and network intrusions, which bring challenges such as lack of privacy, integrity, trust, and centralization. This research aims to implement an Artificial Intelligence-based Lightweight Blockchain Security Model (AILBSM) to ensure privacy and security of IIoT systems. This novel model is meant to address issues that can occur with security and privacy when dealing with Cloud-based IIoT systems that handle data in the Cloud or on the Edge of Networks (on-device). The novel contribution of this paper is that it combines the advantages of both lightweight blockchain and Convivial Optimized Sprinter Neural Network (COSNN) based AI mechanisms with simplified and improved security operations. Here, the significant impact of attacks is reduced by transforming features into encoded data using an Authentic Intrinsic Analysis (AIA) model. Extensive experiments are conducted to validate this system using various attack datasets. In addition, the results of privacy protection and AI mechanisms are evaluated separately and compared using various indicators. By using the proposed AILBSM framework, the execution time is minimized to 0.6 seconds, the overall classification accuracy is improved to 99.8%, and detection performance is increased to 99.7%. Due to the inclusion of auto-encoder based transformation and blockchain authentication, the anomaly detection performance of the proposed model is highly improved, when compared to other techniques. [ABSTRACT FROM AUTHOR]

Autonomous transportation in logistics is the prime focus, with further deep dive into its application across various aspects of the supply chain. In the next article, the authors present an optimized least square support vector machine to improve load forecasting in autonomous vehicles. The next article presents a predictive traffic model and intelligent energy management systems for autonomous vehicles. [Extracted from the article]