Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision‐making (DM), these systems have found wide‐ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six different applications have been discussed, including trustworthy AI in education, environmental science, 5G‐based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.

Segmentation of white blood cells in digital haematology microscope images represents one of the major tools in the diagnosis and evaluation of blood disorders. Pathological examinations are being the gold standard in many haematology and histophathology, and also play a key role in the diagnosis of diseases. In clinical diagnosis, white blood cells are analysed by pathologists from peripheral blood smears samples of patients. This analysis is mainly based on morphological features and characteristics of the white blood cells and their nuclei and cytoplasm, including, shapes, sizes, colours, textures, maturity stages and staining processes. Recently, Computer Aided Diagnosis techniques have been rapidly growing in the digital haematology area related to white blood cells, and their nuclei and cytoplasm detection, as well as their segmentation and classification techniques. In digital haematology image analysis, these techniques have played and will continue to play, a vital role for providing traceable clinical information, consolidating pertinent second opinions, and minimizing human intervention. This study outlines, discusses, and introduces the major trends from a particular review of detection and segmentation methods for white blood cells and their nuclei and cytoplasm from digital haematology microscope images.

Purpose: The use of wearable devices to monitor aspects of personal health is increasing. The Fitbit is an example of a popular device used for this purpose. It is unknown whether users’ privacy (i.e. sensitive data collected from wearable devices) would be leaked via unauthorized access. So, this investigation will answer the following questions; are the data transmissions protected against unauthorised access or modification? what data are transmitted between the device and the server? how much data can be collected by unauthorized access? Method: This paper describes an investigation into data access in the Fitbit Blaze and, specifically, whether this is possible without connecting to the Fitbit server. A Man-In-The-Middle (MITM) attack was used in this investigation. Result: In this experiment, the firmware image, transferred when the device connects to the Fitbit server, is first captured and analysed to obtain data. This was done to attempt to identify the encryption method and obtain the unique device MAC address. Secondly, some fitness data, namely, the authentication key, the cryptographic key and the Nonce, were extracted from the Fitbit application. We attempted to connect the Fitbit Blaze device and the Fitbit application directly without connecting via the Fitbit server. We also attempted direct access to the Fitbit Blaze using a charger cable. In addition, Fitbit Java files were extracted from the Fitbit application. Conclusion: Finally, the outcomes of this investigation are compared with investigations into other Fitbit devices in the previous research.

There exists a growing interest from the clinical practice research communities in the development of methods to automate HEp-2 stained cells classification procedure from histopathological images. Challenges faced by these methods include variations in cell densities and cell patterns, overfitting of features, large-scale data volume and stained cells. In this paper, a multi-class multilayer perceptron technique is adapted by adding a new hidden layer to calculate the variation in the mean, scale, kurtosis and skewness of higher order spectra features of the cell shape information. The adapted technique is then jointly trained and the probability of classification calculated using a Softmax activation function. This method is proposed to address overfitting, stained and large-scale data volume problems, and classify HEp-2 staining cells into six classes. An extensive experimental analysis is studied to verify the results of the proposed method. The technique has been trained and tested on the dataset from ICPR-2014 and ICPR-2016 competitions using the Task-1. The experimental results have shown that the proposed model achieved higher accuracy of 90.3% (with data augmentation) than of 87.5% (with no data augmentation). In addition, the proposed framework is compared with existing methods, as well as, the results of methods using in ICPR2014 and ICPR2016 competitions.The results demonstrate that our proposed method effectively outperforms recent methods.

In multi-tier network systems, custom applications, Web services and platform environments, storing data and information assets becomes a challenge for any organisation. Although there are different methods to secure network systems, the best way to test the level of security is to conduct penetration testing. In this paper, we describe how we performed live penetration testing for a particular network, namely, 192.168.3.0/24 (Case Study) by identifying the system vulnerabilities to enable its penetration. After compromising the system, critical data (Flags) must be found, indicating our successful penetration. As professional penetration testers, we used an arsenal of penetration testing tools utilised by malicious actors on the internet, such as Nmap, Nessus, Sparta and Metasploit, etc. Typically, much effort was employed on reconnaissance & scanning phases, rather than system exploration, due to their importance in identifying security vulnerabilities in the system environment. The vulnerability analysis highlighted the most critical threats, which token is an advantage to gain access, namely, FTP services, HTTP, and human errors. However, comprising the system is not sufficient because the critical data (Flag) generally requires the administrators rights. Consequently, teams often examine the system to find a way to escalate privilege to the root level. Furthermore, some critical data (Flags) require decryption algorithms or the analysis of captured packets to make them readable. We found eight Flags and identified a system security breach. Mitigation strategies addressing the identified vulnerabilities are recommended to ensure the given networks are secured against future attacks.