This research study describes how machine learning techniques can be employed to segment human pupils. The objective is to recognize and isolate the pupil region in ocular images. The aspects of a methodology are data...
This research study describes how machine learning techniques can be employed to segment human pupils. The objective is to recognize and isolate the pupil region in ocular images. The aspects of a methodology are data set preparation, feature extraction, machine learning model selection, model training, model optimization, model testing, model evaluation, and finally post-processing. during preprocessing, this identifies anddesignates pupil regions in the dataset to obtain high-quality eye images. This compares two machine learning models, the Support Vector Machine (SVM) and the random Forest, after identifying the most significant image characteristics. After being trained and fine-tuned using the dataset, models are evaluated based on metrics such as precision, recall, F1 score, and accuracy. In post-processing, the segmented pupil regions are modified after the initial segmentation. random Forest attained 96.27 accuracy, while Support Vector Machine (SVM) obtained 95.17. This research contributes to the advancement of computer vision and is beneficial for software that employs human vision.
One potential use of autoencoders in healthcare is the identification of anomalies in electrocardiogram (ECG) readings. The primary objective of this research is to design a reliable system that can detect irregularit...
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ISBN:
(数字)9798350374957
ISBN:
(纸本)9798350374964
One potential use of autoencoders in healthcare is the identification of anomalies in electrocardiogram (ECG) readings. The primary objective of this research is to design a reliable system that can detect irregularities in electrocardiogram data automatically. The goal is to improve the efficiency and accuracy of anomaly detection using autoencoders so that cardiovascular problems may be diagnosed and treated early. The objective is to prove that the suggested approach can correctly detect a variety of abnormalities, such as arrhythmias and other cardiac abnormalities, using electrocardiogram (ECG) data by conducting extensive experiments and validations. This study aims to provide a dependable tool for healthcare practitioners to evaluate ECG data effectively and quickly detect possible health issues, therefore contributing to the improvement of medical diagnostics. The goal of our effort is to make clinical decision-making in cardiac care settings more effective and to improve patient outcomes. The PTB diagnostic ECG database yielded five results, ranging from 0.57 to 0.92, for each of the five patients in the sample.
The overflow of water from a lake orriver usually causes flooding. Sometimes, a dam breach might result in the unexpectedrelease of vast quantities of water. Some of the water seeps into the ground, flooding the reg...
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The overflow of water from a lake orriver usually causes flooding. Sometimes, a dam breach might result in the unexpectedrelease of vast quantities of water. Some of the water seeps into the ground, flooding the region. In a station, rivers are involving the riverbanks. Along with a lack of goods and houses, businesses, and offices, street infrastructure floods contain bacteria, sewage from waste sites, and chemical spills, which later cause a number of diseases. The rate of change in river stage in real time, which can assist signal the gravity and immediacy of this hazard, is a crucial piece of information for flood predictions. Understanding the type of storm that produced the moisture, including its duration, strength, and actual extent, is important fordetermining the possible severity of the flood. In this system, an Arduino Uno is connected to four separate sensors: a humidity sensor, a flow sensor, a float sensor, and an ultrasonic sensor. The float sensordetects when the water is full. With the aid of IOT, these sensor combinations are utilised to predict floods, alert the appropriate authorities, and sound an immediate alarm in adjacent communities to rapidly relay information about potential floods. These sensors provide data via the IOT’s WiFi module. When flooding conditions are detected, the system warns the nearby villages and places and estimates how long it would take for help to reach at a particular location. The technology also determines when it might be considered a flood and gives them a window of time to leave in case it does.
The worldwide risk of droughts to water security and agricultural sustainability is growing. Using Internet of Things (IoT) technology and a Support Vector Machine (SVM) classifier to create drought-prone early warnin...
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ISBN:
(数字)9798350371567
ISBN:
(纸本)9798350371574
The worldwide risk of droughts to water security and agricultural sustainability is growing. Using Internet of Things (IoT) technology and a Support Vector Machine (SVM) classifier to create drought-prone early warning systems is an innovative solution to these difficulties. Better waterresource management andresilience in water-scarce locations are the study goals. The IoT infrastructure collects real-time data from sensors strategically positioned over the research area to monitor soil moisture, weather, and water levels. SVM classifiers, which are effective in classification, get this large dataset. The SVM model uses data to predict and classify drought events, alerting decision-makers and stakeholders. The system shows that this integrated strategy works. The IoT-SVM early warning system reliably predicts droughts and alerts authorities in advance, enabling proactive mitigation and intervention. The system's drought prediction accuracy helps drought-prone areas achieve water security andresilience by allocating resources, managing water sustainably, andreducing effects on agriculture and people. It uses IoT and machine learning to improve drought preparation andresponse, improving long-term water sustainability andresilience in water-scarce areas.
Human depression Prediction is essential for several reasons, primarily centered around improving mental health outcomes and providing timely interventions. Firstly, early detection of depression allows for prompt and...
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ISBN:
(数字)9798350375237
ISBN:
(纸本)9798350375244
Human depression Prediction is essential for several reasons, primarily centered around improving mental health outcomes and providing timely interventions. Firstly, early detection of depression allows for prompt and targeted interventions, enabling individuals to receive appropriate support and treatment before their condition worsens. The Human depression Prediction Scheme (HdPS) introduces an innovative method for predicting depression by synergizing the capabilities of deep learning through the LeNet architecture with the optimization prowess of the Grey Wolf Optimization (GWO) algorithm. The proposed scheme achieves a commendable accuracy of 95%, attesting to its effectiveness in discerning depressive tendencies. Notably, the HdPS is implemented in Google Colab, emphasizing its accessibility and ease of use. This integration of advanced technologies and optimization techniques positions HdPS as a promising tool for early detection of depression, offering both high accuracy and practical implementation in a widely accessible computing environment.
In the industrial sector, unplanned equipment failures can lead to significant financial losses, safety hazards, and operational inefficiencies. Traditional maintenance strategies, such as reactive maintenance (repair...
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ISBN:
(数字)9798350354218
ISBN:
(纸本)9798350354225
In the industrial sector, unplanned equipment failures can lead to significant financial losses, safety hazards, and operational inefficiencies. Traditional maintenance strategies, such as reactive maintenance (repair after failure) and preventive maintenance (scheduled maintenance), often prove inadequate in addressing these issues. This not only minimizes downtime but also optimizes maintenance resources and extends the lifespan of equipment. Machine learning models used in Predictive maintenance (PdM) are designed to analyze large volumes of data generated by industrial equipment. reinforcement learning optimizes maintenance schedules by learning from interactions with the environment, balancing the trade-off between maintenance costs and equipment reliability. Black-box models, such as deep learning, often provide high accuracy but lack transparency. Explainable AI (XAI) techniques are being developed to make these models more interpretable, enabling users to gain insights into the decision-making process and build confidence in the predictions. The deployment of ML models in industrial environments also requires seamless integration with existing systems. This involves addressing issues related to real-time data processing, scalability, and model retraining. Edge computing and cloud-based solutions are being explored to handle large-scale data processing and storage, while online learning techniques allow models to adapt to new data continuously. This paper presents several case studies from industries such as manufacturing, energy, transportation, and healthcare, illustrating the successful application of ML in PdM. In manufacturing, ML models have been used to predict failures in machinery such as turbines, compressors, and conveyor belts, leading to significant reductions in downtime and maintenance costs. The energy sector has employed ML for the maintenance of critical infrastructure, including power grids and wind turbines, enhancing operational efficienc
Background: Anti-citrullinated protein antibodies (ACPAs) are a hallmark of rheumatoid arthritis (rA). However, due to scarcity of the antigen-specific B cell population, the transcriptome profile of ACPA-expressing B...
Background: Anti-citrullinated protein antibodies (ACPAs) are a hallmark of rheumatoid arthritis (rA). However, due to scarcity of the antigen-specific B cell population, the transcriptome profile of ACPA-expressing B cells has not yet been revealed. Objectives: We aimed to clarify transcriptomic features of ACPA-expressing B cells through single-cell rNA-seq. Methods: We recruited ten clinically suspect arthralgia patients, ten untreated early rA patients, and ten established, methotrexate-treatedrA patients. Peripheral blood (PB) ACPA-enriched B cells (ACPA+), tetanus toxoid (TT)-enriched B cells (TT+), and control antigen-negative ACPA- B cells were purified by flow cytometry using tetramer staining (Figure 1A). Additionally, ACPA-enriched and non-specific B cells were sorted from synovial fluid (SF) of two rA patients. Full-length Smart-seq2 single-cell rNA-seq was performed and transcriptome analysis was conducted using the Seurat pipeline with modifications. B cell receptorreconstruction and clonal analysis were performed using the BraCer pipeline. Monoclonal antibodies were experimentally generated using the rATP-Ig method from fractions of ACPA+ and TT+ B cells and tested for specificity by ELISA. results: After quality control based on transcriptome data, we obtained 494, 453, and 319 cells from the ACPA-, ACPA+, and TT+ PB B cell population, respectively. Likewise, 47 and 35 cells were present in the ACPA- and ACPA+ SF B cell population. Enrichment of ACPA+ and TT+ B cells in both respective populations was evidenced by the generation of monoclonal antibodies of sequenced B cell receptors. Unsupervised clustering analysis identified 7 B cell clusters (Figure 1B). The PB ACPA+ B cell population was heterogeneous, but most of the synovial ACPA+ B cell population was assigned to a single B cell cluster (C7, Figure 1C). The PB ACPA+ memory B cell population was characterized by high expression of Cd11c+ atypical B cell genes such as ITGAX, FGr , and ITGB2 (F
Huge energy utilization information of structures is created with the improving information technology. The energy utilization information is processed by an energy depletion observing system through the wireless sens...
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Huge energy utilization information of structures is created with the improving information technology. The energy utilization information is processed by an energy depletion observing system through the wireless sensor network (WSN). Conventional security approaches can face external attacks. Nevertheless, they cannot proficiently solve interior attacks triggered by the captured nodes. Energy-efficient anddependable Clusterrouting (EPdC) in WSN. This approach aims to reduce energy expenditure and enhance reliable routing in WSNs. The highest energy and highest available bandwidth are selected as a cluster head (CH). In every cluster, the selected CH has received the observed information from the cluster member and then forwarded it to the BS through a relay node. The forwarderrelay node is selected based on the average energy faith and transmission faith during data transmission. The whole faith is greater than the average value that node is selected as a data forwarder from cluster to BS in the WSN. Furthermore, the Path Optimization Algorithm (POA) is used to detect the obstacle and build the obstacle-aware route efficiently. The simulation results explain why the proposed method has betterresidual energy. In addition, it minimized both the network delay and the packet loss rate in the WSN.
Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causi...
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ISBN:
(数字)9798350377972
ISBN:
(纸本)9798350377989
Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining ourresearch's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure anddependable.
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