Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Deep Neural Networks for Network Intrusion Detection

Networks have become an indispensable part of people's lives. With the rapid development of new technologies such as 5G and Internet of Things, people are increasingly dependent on networks, and the scale and complexity of networks are ever-growing. As a result, cyber threats are becoming more and more diverse, frequent and sophisticated, which imposes great threats to the massive networked society. The confidential information of the network users can be leaked; The integrity of data transferred over the network can be tampered; And the computing infrastructures connected to the network can be attacked. Therefore, network intrusion detection system (NIDS) plays a crucial role in offering the modern society a secure and reliable network communication environment. Rule-based NIDSs are effective in identifying known cyber-attacks but ineffective for novel attacks, and hence are unable to cope with the ever-evolving threat landscape today. Machine learning (ML)-based NIDSs with intelligent and automated capabilities, on the other hand, can recognize both known and unknown attacks. Traditional ML-based designs achieve a high threat detection performance at the cost of a large number of false alarms, leading to alert fatigue. Advanced deep learning (DL)-based designs with deep neural networks can effectively mitigate this problem and accomplish better generalization capability than the traditional ML-based NIDSs. However, existing DL-based designs are not mature enough and there is still large room for improvement. To tackle the above problems, in this thesis, we first propose a two-stage deep neural network architecture, DualNet, for network intrusion detection. DualNet is constructed with a general feature extraction stage and a crucial feature learning stage. It can effectively reuse the spatial-temporal features in accordance with their importance to facilitate the entire learning process and mitigate performance degradation problem occurred in deep learning. DualNet is evaluated on a traditional popular NSL-KDD dataset and a modern near-real-world UNSW-NB15 dataset, which shows a high detection accuracy that can be achieved by DualNet. Based on DualNet, we then propose an enhanced design, EnsembleNet. EnsembleNet is a deep ensemble neural network model, which is built with a set of specially designed deep neural networks that are integrated by an aggregation algorithm. The model also has an alert-output enhancement design to facilitate security team's response to the intrusions and hence reduce security risks. EnsembleNet is evaluated on two modern datasets, a near-real-world UNSW-NB15 dataset and a more recent and comprehensive TON_IoT dataset, which shows that EnsembleNet has a high generalization capability. Our evaluations on the UNSW-NB15 dataset that is close to the real-world network traffic demonstrate that DualNet and EnsembleNet outperform state-of-the-art ML-based designs by achieving higher threat detection performance while keeping lower false alarm rate, which also demonstrates that deep neural networks have great application potential in network intrusion detection

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

Colombus: providing personalized recommendations for drifting user interests

The query formulationg process if often a problematic activity due to the cognitive load that it imposes to users. This issue is further amplified by the uncertainty of searchers with regards to their searching needs and their lack of training on effective searching techniques. Also, given the tremendous growth of the world wide web, the amount of imformation users find during their daily search episodes is often overwhelming. Unfortunatelly, web search engines do not follow the trends and advancements in this area, while real personalization features have yet to appear. As a result, keeping up-to-date with recent information about our personal interests is a time-consuming task. Also, often these information requirements change by sliding into new topics. In this case, the rate of change can be sudden and abrupt, or more gradual. Taking into account all these aspects, we believe that an information assistant, a profile-aware tool capable of adapting to users’ evolving needs and aiding them to keep track of their personal data, can greatly help them in this endeavor. Information gathering from a combination of explicit and implicit feedback could allow such systems to detect their search requirements and present additional information, with the least possible effort from them. In this paper, we describe the design, development and evaluation of Colombus, a system aiming to meet individual needs of the searchers. The system’s goal is to pro-actively fetch and present relevant, high quality documents on regular basis. Based entirely on implicit feedback gathering, our system concentrates on detecting drifts in user interests and accomodate them effectively in their profiles with no additional interaction from their side. Current methodologies in information retrieval do not support the evaluation of such systems and techniques. Lab-based experiments can be carried out in large batches but their accuracy often questione. On the other hand, user studies are much more accurate, but setting up a user base for large-scale experiments is often not feasible. We have designed a hybrid evaluation methodology that combines large sets of lab experiments based on searcher simulations together with user experiments, where fifteen searchers used the system regularly for 15 days. At the first stage, the simulation experiments were aiming attuning Colombus, while the various component evaluation and results gathering was carried out at the second stage, throughout the user study. A baseline system was also employed in order to make a direct comparison of Colombus against a current web search engine. The evaluation results illustrate that the Personalized Information Assistant is effective in capturing and satisfying users’ evolving information needs and providing additional information on their behalf

Modeling Movement Disorders in Parkinson's Disease using Computational Intelligence

Parkinson's is the second most common neurodegenerative disease after Alzheimer's Disease and affects 127,000 people in the UK alone. Providing the most appropriate treatment pathway can prove challenging owing to the difficulty in obtaining an accurate diagnosis; due to its similarity in symptoms with other neurodegenerative diseases, it is estimated that in the United Kingdom around 24% of cases are misdiagnosed by general neurologists. A means of providing an accurate and early diagnosis of Parkinson's Disease would thereby enable a more effective management of the disease, increased quality of life for patients, and reduce costs to the healthcare system. The work described in this thesis details progress towards this goal by modeling movement disorders in the form of positional data recorded from simple movement tasks, building towards a fully objective diagnostic system without requiring any specialist domain knowledge. This is accomplished by modeling established movement disorder markers using Evolutionary Algorithms to train ensembles, before implementing feature design strategies with both Genetic Programming and Echo State Networks. The findings of this study make an important contribution to the area of data mining, including: the demonstration that Computational Intelligence-based feature design strategies can be competitive to conventional models using features extracted with expert domain knowledge; a thorough survey of evolutionary ensemble research; and the development of a novel evolutionary ensemble approach comprising traditional single objective Evolutionary Algorithm. Furthermore, an extension to a Genetic Programming feature design strategy for periodic time series is detailed, in addition to demonstrating that Echo State Networks can be directly applied to time series classification as a feature design method. This research was carried out in the context of building an applied diagnostic aid and required developing models with means of indicating the most discriminatory aspects of the sequence data, thereby facilitating inference of the precise mechanics of movement disorders to clinical neurologists

Association between central sensitization and gait in chronic low back pain:Insights from a machine learning approach

BACKGROUND: Central sensitization (CS) is often present in patients with chronic low back pain (CLBP). Gait impairments due to CLBP have been extensively reported; however, the association between CS and gait is unknown. The present study examined the association between CS and CLBP on gait during activities of daily living. METHOD: Forty-two patients with CLBP were included. CS was assessed through the Central Sensitization Inventory (CSI), and patients were divided in a low and high CS group (23 CLBP- and 19 CLBP+, respectively). Patients wore a tri-axial accelerometer device for one week. From the acceleration signals, gait cycles were extracted and 36 gait outcomes representing quantitative and qualitative characteristics of gait were calculated. A Random Forest was trained to classify CLBP- and CLBP + based on the gait outcomes. The maximum Youden index was computed to measure the diagnostic test's ability and SHapley Additive exPlanations (SHAP) indexed the gait outcomes' importance to the classification model. RESULTS: The Random Forest accurately (84.4%) classified the CLBP- and CLBP+. Youden index was 0.65, and SHAP revealed that the gait outcomes' important to the classification model were related to gait smoothness, stride frequency variability, stride length variability, stride regularity, predictability, and stability. CONCLUSIONS: CLBP- and CLBP + patients had different motor control strategies. Patients in the CLBP- group presented with a more "loose control", with higher gait smoothness and stability, while CLBP + patients presented with a "tight control", with a more regular, less variable, and more predictable gait pattern

人の行動分類のための教師なし転移学習

筑波大学 (University of Tsukuba)201