Digital dual test syphilis/HIV detection based on Fourier Descriptors of Cyclic Voltammetry curves.

BACKGROUND: Effective and timely detection is vital for mitigating the severe impacts of Sexually Transmitted Infections (STI), including syphilis and HIV. Cyclic Voltammetry (CV) sensors have shown promise as diagnostic tools for these STI, offering a pathway towards cost-effective solutions in primary health care settings. OBJECTIVE: This study aims to pioneer the use of Fourier Descriptors (FDs) in analyzing CV curves as 2D closed contours, targeting the simultaneous detection of syphilis and HIV. METHODS: Raw CV signals are filtered, resampled, and transformed into 2D closed contours for FD extraction. Essential shape characteristics are captured through selected coefficients. A complementary geometrical analysis further extracts features like curve areas and principal axes lengths from CV curves. A Mahalanobis Distance Classifier is employed for differentiation between patient and control groups. RESULTS: The evaluation of the proposed method revealed promising results with classification performance metrics such as Accuracy and F1-Score consistently achieving values rounded to 0.95 for syphilis and 0.90 for HIV. These results underscore the potential efficacy of the proposed approach in differentiating between patient and control samples for STI detection. CONCLUSION: By integrating principles from biosensors, signal processing, image processing, machine learning, and medical diagnostics, this study presents a comprehensive approach to enhance the detection of both syphilis and HIV. This setts the stage for advanced and accessible STI diagnostic solutions.

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.

Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models-without resorting to deep learning-is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection.

Evaluating Time Influence over Performance of Machine-Learning-Based Diagnosis: A Case Study of COVID-19 Pandemic in Brazil.

Efficiently recognising severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) symptoms enables a quick and accurate diagnosis to be made, and helps in mitigating the spread of the coronavirus disease 2019. However, the emergence of new variants has caused constant changes in the symptoms associate with COVID-19. These constant changes directly impact the performance of machine-learning-based diagnose. In this context, considering the impact of these changes in symptoms over time is necessary for accurate diagnoses. Thus, in this study, we propose a machine-learning-based approach for diagnosing COVID-19 that considers the importance of time in model predictions. Our approach analyses the performance of XGBoost using two different time-based strategies for model training: month-to-month and accumulated strategies. The model was evaluated using known metrics: accuracy, precision, and recall. Furthermore, to explain the impact of feature changes on model prediction, feature importance was measured using the SHAP technique, an XAI technique. We obtained very interesting results: considering time when creating a COVID-19 diagnostic prediction model is advantageous.

Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis.

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

Routing and Scheduling Algorithms for WirelessHARTNetworks: A Survey.

Wireless communication is a trend nowadays for the industrial environment. A number of different technologies have emerged as solutions satisfying strict industrial requirements (e.g., WirelessHART, ISA100.11a, WIA-PA). As the industrial environment presents a vast range of applications, adopting an adequate solution for each case is vital to obtain good performance of the system. In this context, the routing and scheduling schemes associated with these technologies have a direct impact on important features, like latency and energy consumption. This situation has led to the development of a vast number of routing and scheduling schemes. In the present paper, we focus on the WirelessHART technology, emphasizing its most important routing and scheduling aspects in order to guide both end users and the developers of new algorithms. Furthermore, we provide a detailed literature review of the newest routing and scheduling techniques forWirelessHART, discussing each of their features. These routing algorithms have been evaluated in terms of their objectives, metrics, the usage of theWirelessHART structures and validation method. In addition, the scheduling algorithms were also evaluated by metrics, validation, objectives and, in addition, by multiple superframe support, as well as by the redundancy method used. Moreover, this paper briefly presents some insights into the main WirelessHART simulation modules available, in order to provide viable test platforms for the routing and scheduling algorithms. Finally, some open issues in WirelessHART routing and scheduling algorithms are discussed.

Research trends in wireless visual sensor networks when exploiting prioritization.

The development of wireless sensor networks for control and monitoring functions has created a vibrant investigation scenario, where many critical topics, such as communication efficiency and energy consumption, have been investigated in the past few years. However, when sensors are endowed with low-power cameras for visual monitoring, a new scope of challenges is raised, demanding new research efforts. In this context, the resource-constrained nature of sensor nodes has demanded the use of prioritization approaches as a practical mechanism to lower the transmission burden of visual data over wireless sensor networks. Many works in recent years have considered local-level prioritization parameters to enhance the overall performance of those networks, but global-level policies can potentially achieve better results in terms of visual monitoring efficiency. In this paper, we make a broad review of some recent works on priority-based optimizations in wireless visual sensor networks. Moreover, we envisage some research trends when exploiting prioritization, potentially fostering the development of promising optimizations for wireless sensor networks composed of visual sensors.

Availability issues in wireless visual sensor networks.

Wireless visual sensor networks have been considered for a large set of monitoring applications related with surveillance, tracking and multipurpose visual monitoring. When sensors are deployed over a monitored field, permanent faults may happen during the network lifetime, reducing the monitoring quality or rendering parts or the entire network unavailable. In a different way from scalar sensor networks, camera-enabled sensors collect information following a directional sensing model, which changes the notions of vicinity and redundancy. Moreover, visual source nodes may have different relevancies for the applications, according to the monitoring requirements and cameras' poses. In this paper we discuss the most relevant availability issues related to wireless visual sensor networks, addressing availability evaluation and enhancement. Such discussions are valuable when designing, deploying and managing wireless visual sensor networks, bringing significant contributions to these networks.

Reliability and availability evaluation of Wireless Sensor Networks for industrial applications.

Wireless Sensor Networks (WSN) currently represent the best candidate to be adopted as the communication solution for the last mile connection in process control and monitoring applications in industrial environments. Most of these applications have stringent dependability (reliability and availability) requirements, as a system failure may result in economic losses, put people in danger or lead to environmental damages. Among the different type of faults that can lead to a system failure, permanent faults on network devices have a major impact. They can hamper communications over long periods of time and consequently disturb, or even disable, control algorithms. The lack of a structured approach enabling the evaluation of permanent faults, prevents system designers to optimize decisions that minimize these occurrences. In this work we propose a methodology based on an automatic generation of a fault tree to evaluate the reliability and availability of Wireless Sensor Networks, when permanent faults occur on network devices. The proposal supports any topology, different levels of redundancy, network reconfigurations, criticality of devices and arbitrary failure conditions. The proposed methodology is particularly suitable for the design and validation of Wireless Sensor Networks when trying to optimize its reliability and availability requirements.

A survey on multimedia-based cross-layer optimization in visual sensor networks.

Visual sensor networks (VSNs) comprised of battery-operated electronic devices endowed with low-resolution cameras have expanded the applicability of a series of monitoring applications. Those types of sensors are interconnected by ad hoc error-prone wireless links, imposing stringent restrictions on available bandwidth, end-to-end delay and packet error rates. In such context, multimedia coding is required for data compression and error-resilience, also ensuring energy preservation over the path(s) toward the sink and improving the end-to-end perceptual quality of the received media. Cross-layer optimization may enhance the expected efficiency of VSNs applications, disrupting the conventional information flow of the protocol layers. When the inner characteristics of the multimedia coding techniques are exploited by cross-layer protocols and architectures, higher efficiency may be obtained in visual sensor networks. This paper surveys recent research on multimedia-based cross-layer optimization, presenting the proposed strategies and mechanisms for transmission rate adjustment, congestion control, multipath selection, energy preservation and error recovery. We note that many multimedia-based cross-layer optimization solutions have been proposed in recent years, each one bringing a wealth of contributions to visual sensor networks.

The coverage problem in video-based wireless sensor networks: a survey.

Wireless sensor networks typically consist of a great number of tiny low-cost electronic devices with limited sensing and computing capabilities which cooperatively communicate to collect some kind of information from an area of interest. When wireless nodes of such networks are equipped with a low-power camera, visual data can be retrieved, facilitating a new set of novel applications. The nature of video-based wireless sensor networks demands new algorithms and solutions, since traditional wireless sensor networks approaches are not feasible or even efficient for that specialized communication scenario. The coverage problem is a crucial issue of wireless sensor networks, requiring specific solutions when video-based sensors are employed. In this paper, it is surveyed the state of the art of this particular issue, regarding strategies, algorithms and general computational solutions. Open research areas are also discussed, envisaging promising investigation considering coverage in video-based wireless sensor networks.