The growth of the Internet and related technologies has enabled the development of a new breed of dynamic websites, applications and software products that are growing rapidly in use and that have had a great impact on many businesses. These technologies need to be continuously evaluated by usability evaluation methods (UEMs) to measure their efficiency and effectiveness, to assess user satisfaction, and ultimately to improve their quality. However, estimating the sample sizes for these methods has become the source of considerable debate at usability conferences. This paper aims to determine an appropriate sample size through empirical studies on the social network and educational domains by employing three types of UEM; it also examines further the impact of sample size on the findings of usability tests. Moreover, this paper quantifies the sample size required for the Domain Specific-to-context Inspection (DSI) method, which itself is developed through an adaptive framework. The results show that there is no certain number of participants for finding all usability problems; however, the rule of 16 4 users gains much validity in user testing. The magic number of five evaluators fails to find 80% of problems in heuristic evaluation, whereas three evaluators are enough to find 91% of usability problems in the DSI method.

Impressive technological advancements over the past decades commenced significant advantages in the maritime industry sector and elevated commercial, operational, and financial benefits. However, technological development introduces several novel risks that pose serious and potential threats to the maritime industry and considerably impact the maritime industry. Keeping in view the importance of maritime cyber security, this study presents the cyber security threats to understand their impact and loss scale. It serves as a guideline for the stakeholders to implement effective preventive and corrective strategies. Cyber security risks are discussed concerning maritime security, confidentiality, integrity, and availability, and their impact is analyzed. The proneness of the digital transformation is analyzed regarding the use of internet of things (IoT) devices, modern security frameworks for ships, and sensors and devices used in modern ships. In addition, risk assessment methods are discussed to determine the potential threat and severity along with the cyber risk mitigation schemes and frameworks. Possible recommendations and countermeasures are elaborated to alleviate the impact of cyber security breaches. Finally, recommendations about the future prospects to safeguard the maritime industry from cyber-attacks are discussed, and the necessity of efficient security policies is highlighted.

Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes are required to sustain food quality levels. We have designed a prototype to track food quality and to manage storage systems at home. Initially, we have employed a Convolutional Neural Network (CNN) model to detect the type of fruit and veggies. Then the proposed system monitors the gas emission level, humidity level, and temperature of fruits and veggies by using sensors and actuators to check the food spoilage level. This would additionally control the environment and avoid food spoilage wherever possible. Additionally, the food spoilage level is informed to the customer by an alert message sent to their registered mobile numbers based on the freshness and condition of the food. The model employed proved to have an accuracy rate of 95%. Finally, the experiment is successful in increasing the shelf life of some categories of food by 2 days.

Anomaly-based intrusion detection systems (IDSs) have been deployed to monitor network activity and to protect systems and the Internet of Things (IoT) devices from attacks (or intrusions). The problem with these systems is that they generate a huge amount of inappropriate false alarms whenever abnormal activities are detected and they are not too flexible for a complex environment. The high-level rate of the generated false alarms reduces the performance of IDS against cyber-attacks and makes the tasks of the security analyst particularly difficult and the management of intrusion detection process computationally expensive. We study here one of the challenging aspects of computer and network security and we propose to build a detection model for both known and unknown intrusions (or anomaly detection) via a novel nonparametric Bayesian model. The design of our framework can be extended easily to be adequate for IoT technology and notably for intelligent smart city web-based applications. In our method, we learn the patterns of the activities (both normal and anomalous) through a Bayesian-based MCMC inference for infinite bounded generalized Gaussian mixture models. Contrary to classic clustering methods, our approach does not need to specify the number of clusters, takes into consideration the uncertainty via the introduction of prior knowledge for the parameters of the model, and permits to solve problems related to over- and under-fitting. In order to get better clustering performance, feature weights, model’s parameters, and the number of clusters are estimated simultaneously and automatically. The developed approach was evaluated using popular data sets. The obtained results demonstrate the efficiency of our approach in detecting various attacks.

The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices.