The increased use of Electronic Health Records (EHRs) has increased access to patient information in medical facilities, but has also raised long-term concerns regarding the security, confidentiality, and partial control of records. In most work environments, medical information is scattered across hospitals, laboratories, and clinics, hindering transparency and making its sharing and analysis extremely difficult. Most existing systems operate on centralised architectures that are not as hard to manage but can be undermined by data breaches, unauthorised access, and single points of failure. To address these shortcomings, this paper presents a management and prediction model for medical reports that integrates blockchain technology, decentralised storage, and machine learning-based analytics. A permissioned consortium blockchain is responsible for metadata, ownership, and access control of large medical files stored off-chain in the InterPlanetary File System (IPFS) to maximise scalability and efficiency. Anonymised and aggregated data have been analysed using machine learning models to enable predictive analysis without exposing sensitive patient data. The proposed system was tested in controlled experimental scenarios using a simulated healthcare dataset. The results demonstrate improved data integrity, clearer control over access, and greater storage efficiency compared to conventional centralised approaches. Although certain scalability, data availability, and real-world application issues remain, the findings demonstrate that the recommended architecture provides a viable and secure foundation for patient-centred healthcare data management and predictive support.

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Artificial intelligence (AI) has proven to be an asset in reducing human intervention in predicting and decision-making for many applications. Electrification through the addition of both electric vehicles (EVs) and charging stations is resulting in several core challenges, including improving utilisation, increasing availability, and reducing charging times. This paper aims to develop concepts that AI can be trained on to enable applications such as predicting battery life, making accurate charging-time predictions, and identifying the cheapest available charging options for EV owners. Additionally, a model was proposed to predict with available variables to provide major stakeholders, such as customers with EV purchasing timeframes, businesses with suitable chargers, governments for policy updates, and other stakeholders for carbon footprint reduction measures. Although this concept limits data collection from both manufacturers and customers, relaxing government policies to allow data access may lead to improved AI models. Upgrading charging equipment to enable data collection on customer charger utilisation is a challenge from both manufacturers’ and users’ perspectives. Users become conscious of their data privacy while sharing information about their vehicles and charging frequencies. Manufacturers become more conscious of their data privacy when sharing typical battery and other equipment characteristics curves, which are more confidential, to ensure they are not readily available to competitors. The holistic conceptual model developed in this paper served as the basis for AI training. The model offers significant opportunities to learn from other published predictive techniques and data analysis methods for critical infrastructure, thereby increasing the safety, quality, and reliability of electrical power.

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The paper proposes a practical, scalable, and non-intrusive system for the automatic detection of tool wear under real industrial conditions that does not require process metadata (e.g., spindle speed, feed rate) or specialised equipment. The proposed method is based solely on the analysis of triaxial vibration signals and combines multiple signal-processing methods (time, frequency, and time-frequency) to enable deeper analysis. Various time-domain features, such as RMS, standard deviation, kurtosis, and crest factor, are combined with spectral analysis via Welch power spectral density (PSD) estimation and the continuous wavelet transform (CWT) for time-frequency analysis. To make features comparable across machines and operating conditions, the features are combined using median statistics and normalised relative to the median (Δ%). Experimental validation was performed with multiple machines and measurement axes on real industrial datasets that were heavily imbalanced. It was shown that there is a direct and consistent correlation between the condition of worn tools and the overall increase in tool vibration energy, as evidenced by significantly higher RMS and standard deviation values. On the other hand, higher-order statistical measures, such as kurtosis and crest factor, were less consistent when used alone due to their sensitivity to operational variability. Frequency domain analysis showed that the wear of the tool could not only be described by a general rise in energy, but also by the significant increase of certain frequency components in the spectrum. Most notably, the peaks centred at 200 Hz were found to be significantly raised, along with their harmonics at around 600 Hz and 800 Hz, when the tool was worn, and this was true for all the axes of the measurements. These frequencies can thus be considered reliable and repeatable indicators of tool wear. The continuous wavelet transforms (CWT), a complementary method to time-localised time-frequency representations, confirmed the results and, notably, revealed that the signal’s high-energy bursts are time-recurrent and running; thus, they can be seen as a series of bright spots closely packed in time in the scalogram. The strength of the proposal is its rational and transparent integration of several well-known signal-processing methods into a single, coherent concept that encompasses the full spectrum of changes, from the global increase in vibration energy to the localised, frequency-specific excitations that are the hallmark of wear progression. By combining global energy indicators with spectral and time frequency features, the approach enhances diagnostic reliability while improving physical understanding of tool-wear mechanisms. It is robust, practical, and readily transferable, offering strong potential for scalable predictive maintenance applications in machining environments.

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Rolling element bearings are fundamental parts of rotating machinery, and their sudden breakdown may cause abnormal vibrations, an unplanned production halt, and higher maintenance costs. To address this problem, this paper presents a hybrid data-driven approach for the automatic diagnosis and classification of bearing faults using vibration signals from the IMS dataset. The proposed method first divides the raw vibration signals into fixed-length windows and computes seven statistical features to characterise the signals. An unsupervised Isolation Forest technique is then used to detect anomalous signal segments, providing an early warning of potential faults. Thus, it does not require prior knowledge of the normal condition for fault detection. Subsequently, the detected anomalies are classified using two ensemble-based supervised learning models: Extra Trees and XGBoost. The experimental results indicate that the tree-based ensemble classifiers outperform the other models tested. Specifically, XGBoost achieves an F1 score of 97. 41%, whereas Extra Trees achieves an F1 score of nearly 97%, indicating its high potential for accurately detecting different types of bearing faults. The results confirm that the proposed hybrid two-stage model is an efficient and reliable tool for bearing fault diagnosis, supporting early fault detection, and enhancing system reliability in industrial settings.

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We have witnessed a significant amount of fraud and security issues in modern life. Numerous biometric characteristics, such as the eyes, face, fingers, and palms, are used to address these problems. Among these, facial recognition is considered one of the least intrusive methods and is frequently used to identify or verify an individual. Face recognition is one of the most effective applications of computer vision, and has achieved considerable attention in recent years. Deep learning networks have achieved state-of-the-art performance in still-image-based face recognition. Video-based face recognition is a more complex task than still-image-based face recognition due to video quality, pose variation, occlusion, and illumination, and it also entails processing a large volume of data. We address these challenges by developing an efficient deep learning model trained, tested, and evaluated on the YouTube Face Dataset, designed for unconstrained face recognition in videos. In this paper, a deep learning face detection algorithm, Multi-task Cascaded Convolutional Neural Network (MTCNN), is employed to detect and localise faces in videos. Feature extraction and face recognition have been performed by using a convolutional neural network (CNN). This model has been proposed for accurate face detection and recognition from unconstrained video and performs better on the YouTube face dataset. The test accuracy of the proposed model is 93.11%. This work has been conducted to improve face recognition accuracy in the presence of intra-video variations.

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