Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%.

Distributed wireless sensor networks (WSNs) have been implemented in multiple applications. Those networks are intended to support the quality of operations and enhance applications’ productivity and safety. WSNs are constructed of a large amount of sensor nodes that are battery powered. Typically, wireless sensors are deployed in complex terrain which makes battery replacement extremely difficult. Therefore, it is critical to adopt an energy sustainability approach to enhance the lifetime of each sensor node since each node contributes to the lifetime of the entire WSN. In this work, we propose an approach to reduce power consumption in wireless sensors. The approach addresses power reduction in a sensor node at the sensing level, as well as the communication level. First, we propose configuring the microcontroller of the sensor to conserve energy based on the performed tasks. Then, we implement an interface to reduce consumed power by the radio module. Based on the approach, we carried out field experiments and we measure the improvement of power-consumption reduction. The results show that the approach contributes to saving up to 50% of the wasted energy at the sensor node and it improves communication reliability especially when the number of sensors in a network scales.

It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid’s stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method.

The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational stakeholders to accomplish the vision. In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that occur at particular moments during mutual interaction using physiological signals. The self-organizing map technique is implemented to cluster overall organizational emotions to represent organizational competitiveness. The analysis of variance test results indicates no significant difference in age and body mass index for participants exhibiting different emotions. However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological sensors and their measures to analyze emotions for organizational competitiveness. We achieved 99.8% classification accuracy for emotions using the proposed technique. The study precisely identifies the emotions and locates a connection between emotional intelligence and organizational competitiveness (i.e., a positive relationship with employees augments organizational competitiveness).

In today’s rapidly advancing healthcare landscape, integrating Artificial Intelligence (AI) and Machine Learning (ML) has the potential to significantly improve patient care and streamline medical processes. The utilization of confidential patient data to train and develop these technologies, however, raises significant concerns regarding authenticity, security, and privacy. In this study, we introduce MediChainAI, a safe and practical framework that allows patients full ownership over their own health data by integrating Self-Sovereign Identity (SSI), Blockchain, and sophisticated cryptography techniques. By clearly outlining the goals and parameters of this access, MediChainAI allows patients to safely and selectively share data with healthcare providers and researchers. While SSI guarantees that patients have ownership of their data, the framework uses Blockchain technology to keep things transparent and secure. Further, MediChainAI makes use of Merkle trees, which provide verified access to subsets of data without jeopardizing the privacy of the whole dataset. The encryption mechanism, which is based on smart contracts, is a distinctive feature of the framework that allows researchers and medical practitioners controlled and secure access to patient data. In order to improve the accuracy and reliability of medical diagnoses and treatment, this strategy makes sure that only confirmed, legitimate data is utilized to train medical models. A significant step toward safer and more personalized healthcare, MediChainAI encourages ethical and patient-focused innovation by effectively resolving essential issues regarding data security and patient privacy.

Advances in data collection, storage, and processing in e-Health systems have recently increased the importance and popularity of data mining in the health care field. However, the high sensitivity of the handled and shared data, brings a high risk of information disclosure and exposure. It is therefore important to hide sensitive relationships by modifying the shared data. This major information security threat has, therefore, mandated the requirement of hiding/securing sensitive relationships of shared data. As a large number of data mining activities that attempt to identify interesting patterns from databases depend on locating frequent item sets, further investigation of frequent item sets requires privacy-preserving techniques. To solve many difficult combinatorial problems, such as data distribution problem, exact and heuristic algorithms have been used. Exact algorithms are studied and considered optimal for such problems, however they suffer scalability bottleneck, as they are limited to medium-sized instances only. Heuristic algorithms, on the other hand, are scalable, however, they perform poor on security and privacy preservation. This paper proposes a novel heuristic approach based on Formal Concept Analysis (FCA) for enhancing security and privacy preservation of sensitive e-Health information using itemset hiding techniques. Our approach, named FACHS (FCA Hiding Sensitive-itemsets) uses constraints to minimise side effects and asymmetry between the original database and the clean database (minimal distortion on the database). Moreover, our approach does not require frequent itemset extraction before the masking process. This gives the proposed approach an advantage in terms of total availability. We tested our FCAHS heuristic on various reference datasets. Extensive experimental results showed the effectiveness of the proposed masking approach and the time efficiency of itemset extraction, making it very promising for e-Health sensitive data security and privacy.