17 research outputs found
Comparative study of classification algorithms for immunosignaturing data
No full textabstract: Background
High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.
Results
We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found ‘Naïve Bayes’ far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy.
Conclusions
‘Naïve Bayes’ algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.The electronic version of this article is the complete one and can be found online at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-13
Analysing QBER and secure key rate under various losses for satellite based free space QKD
No full textQuantum Key Distribution is a key distribution method that uses the qubits to
safely distribute one-time use encryption keys between two or more authorised
participants in a way that ensures the identification of any eavesdropper. In
this paper, we have done a comparison between the BB84 and B92 protocols and
BBM92 and E91 entanglement based protocols for satellite based uplink and
downlink in low Earth orbit. The expressions for the quantum bit error rate and
the keyrate are given for all four protocols. The results indicate that, when
compared to the B92 protocol, the BB84 protocol guarantees the distribution of
a higher secure keyrate for a specific distance. Similarly, it is observed that
BBM92 ensures higher keyrate in comparison with E91 protocol.Comment: arXiv admin note: text overlap with arXiv:1906.08115 by other author
Understanding Social Media Users' Perceptions of Trigger and Content Warnings
No full textThe prevalence of distressing content on social media raises concerns about users' mental well-being, prompting the use of trigger warnings (TW) and content warnings (CW). However, varying practices across platforms indicate a lack of clarity among users regarding these warnings. To gain insight into how users experience and use these warnings, we conducted interviews with 15 regular social media users. Our findings show that users generally have a positive view of warnings, but there are differences in how they understand and use them. Challenges related to using TW/CW on social media emerged, making it a complex decision when dealing with such content. These challenges include determining which topics require warnings, navigating logistical complexities related to usage norms, and considering the impact of warnings on social media engagement. We also found that external factors, such as how the warning and content are presented, and internal factors, such as the viewer's mindset, tolerance, and level of interest, play a significant role in the user's decision-making process when interacting with content that has TW/CW. Participants emphasized the need for better education on warnings and triggers in social media and offered suggestions for improving warning systems. They also recommended post-trigger support measures. The implications and future directions include promoting author accountability, introducing nudges and interventions, and improving post-trigger support to create a more trauma-informed social media environment.Master of ScienceIn today's world of social media, you often come across distressing content that can affect your mental well-being. To address this concern, platforms and content authors use something called trigger warnings (TW) and content warnings (CW) to alert users about potentially upsetting content. However, different platforms have different ways of using these warnings, which can be confusing for users.
To better understand how people like you experience and use these warnings, we conducted interviews with 15 regular social media users. What we found is that, in general, users have a positive view of these warnings, but there are variations in how they understand and use them.
Using TW/CW on social media can be challenging because it involves deciding which topics should have warnings, dealing with the different rules on each platform, and thinking about how warnings affect people's engagement with content.
We also discovered that various factors influence how people decide whether to engage with warned content. These factors include how the warning and content are presented and the person's own mindset, tolerance for certain topics, and level of interest.
Our study participants highlighted the need for better education about warnings and triggers on social media. They also had suggestions for improving how these warnings are used and recommended providing support to users after they encounter distressing content.
Looking ahead, our findings suggest the importance of holding content creators accountable, introducing helpful tools and strategies, and providing better support to make social media a more empathetic and supportive place for all users
Seismic Data Analysis and Earthquake Prediction with IoT Sensors and SmartGRU Model
No full textTectonic plate movement causes a slow accumulation of stress in the Earth’s lithosphere, especially around plate borders, leading to earthquakes. An earthquake occurs when this stress overcomes friction along a fault or exceeds the strength of the surrounding rock. Accurate earthquake prediction remains challenging due to the complexity of seismic data and the limitations of traditional methods. This creates a pressing need for models capable of real-time analysis and high prediction accuracy. The Internet of Things (IoT) provides a novel method for detecting earthquakes using a variety of sensors to collect vital seismic data, such as latitude, longitude, depth, magnitude, and time. IoT controllers and centralized systems process and analyze this data to enable efficient monitoring and forecasting. Furthermore, with the help of a machine learning model named Bidirectional Gated Recurrent Unit (Bi-GRU), which integrates sophisticated data fusion and advanced machine learning techniques. Our proposed study model, SmartGRU, demonstrates how to improve earthquake prediction systems by combining IoT sensors with a Bi-GRU machine learning model that incorporates an emerging approach
IoT-Driven Gas Safety: Combining Dual-Sensor Technology and Cloud Integration for Automated Risk Mitigation
No full textA gas leak in a home can be very dangerous and cause accidents or illness if it is not found soon enough. Many existing gas detection systems cannot avoid false alarms and delays, which means better, real-time systems are needed. A system that uses an ESP32 microcontroller, two sensors (MQ6 for high sensitivity and NDIR for confirmation), and detects gas leaks using the Internet of Things (IoT) is presented in this paper. The methodology of the system includes simulating sensor readings, code within the microcontroller, and MQTT cloud messages at gas concentrations running from 0 to 10,500 ppm. The simulation adds both sensor noise and delays from the network to reflect real life, as alarms are sounded only after both sensors agree. Tests showed the system stays true to zero false alarms and has detection rates above 95% up to 100% when gases reach over 5500ppm. Furthermore, MQTT provides consistently low communication latency of 26 to 32 milliseconds, which helps make responding to emergencies nearly real-time. The research introduces a new IoT approach that manages accuracy, dependability, and speed for residential gas safety, validated through detailed simulation experiments
NeuroSecure-IoMT: Deep Learning Meets Cyber Defense in the Internet of Medical Things
No full textIintrusion deduction systems (IDS) are crucial to preserving sensitive medical information from cyber threats. However, issues with multi-class intrusion detection include an imbalanced data set, poor accuracy for minority classes, and a lack of flexibility in handling complex real-world situations. To address these issues, we provide a hybrid framework that combines machine learning and deep learning methods to address these problems. The model uses a random forest classifier for anomaly detection after reducing dimensionality using an autoencoder. The Synthetic Minority Oversampling Technique (SMOTE) was used during processing to ensure equitable class representation and reduce class imbalance. A multi-class intrusion detection dataset tailored to healthcare applications was used to thoroughly test the suggested framework, which provides an impressive 99% accuracy rate. In addition to its excellent accuracy, the model addresses important issues in multi-class Intrusion detection by exhibiting remarkable precision for minority classes and consistent performance across all categories. These results highlight the framework\u27s effectiveness in providing dependable and effective normal detection solutions, which makes it ideal for implementation in crucial sectors like healthcare, their accuracy and data security are crucial
Green Growth: AI-Driven Intelligent Farming for Effective Resource Management
No full textEffective fertilizer management plays a critical role in maximizing crop yield while reducing environmental harm and minimizing resource waste. This study presents an IoT-based intelligent fertilizer recommendation system designed to deliver accurate, real-time application guidance. The system integrates NPK sensors for soil nutrient detection, environmental sensors for humidity and temperature monitoring, and rain gauges to collect precipitation data. Data from the field is transmitted through an Arduino microcontroller to a cloud platform. A Random Forest classifier is used to determine the need for fertilization, while a CatBoost regressor estimates the required fertilizer quantity. The system was tested using real-time field data across 22 crop types, achieving 100% accuracy in classification and strong performance in regression tasks. Recommendations are automated and delivered via SMS to streamline field operations. The objective of this study is to develop an automated, sensor-driven fertilizer recommendation system using machine learning for precision agriculture. The novelty lies in the integration of real-time IoT sensing with hybrid AI models to optimize fertilizer use. This approach enhances productivity, reduces input waste, and supports environmentally sustainable farming
IoT-Driven Gas Safety: Combining Dual-Sensor Technology and Cloud Integration for Automated Risk Mitigation
No full textA gas leak in a home can be very dangerous and cause accidents or illness if it is not found soon enough. Many existing gas detection systems cannot avoid false alarms and delays, which means better, real-time systems are needed. A system that uses an ESP32 microcontroller, two sensors (MQ6 for high sensitivity and NDIR for confirmation), and detects gas leaks using the Internet of Things (IoT) is presented in this paper. The methodology of the system includes simulating sensor readings, code within the microcontroller, and MQTT cloud messages at gas concentrations running from 0 to 10,500 ppm. The simulation adds both sensor noise and delays from the network to reflect real life, as alarms are sounded only after both sensors agree. Tests showed the system stays true to zero false alarms and has detection rates above 95% up to 100% when gases reach over 5500ppm. Furthermore, MQTT provides consistently low communication latency of 26 to 32 milliseconds, which helps make responding to emergencies nearly real-time. The research introduces a new IoT approach that manages accuracy, dependability, and speed for residential gas safety, validated through detailed simulation experiments
Green Growth: AI-Driven Intelligent Farming for Effective Resource Management
No full textEffective fertilizer management plays a critical role in maximizing crop yield while reducing environmental harm and minimizing resource waste. This study presents an IoT-based intelligent fertilizer recommendation system designed to deliver accurate, real-time application guidance. The system integrates NPK sensors for soil nutrient detection, environmental sensors for humidity and temperature monitoring, and rain gauges to collect precipitation data. Data from the field is transmitted through an Arduino microcontroller to a cloud platform. A Random Forest classifier is used to determine the need for fertilization, while a CatBoost regressor estimates the required fertilizer quantity. The system was tested using real-time field data across 22 crop types, achieving 100% accuracy in classification and strong performance in regression tasks. Recommendations are automated and delivered via SMS to streamline field operations. The objective of this study is to develop an automated, sensor-driven fertilizer recommendation system using machine learning for precision agriculture. The novelty lies in the integration of real-time IoT sensing with hybrid AI models to optimize fertilizer use. This approach enhances productivity, reduces input waste, and supports environmentally sustainable farming
NeuroSecure-IoMT: Deep Learning Meets Cyber Defense in the Internet of Medical Things
No full textIintrusion deduction systems (IDS) are crucial to preserving sensitive medical information from cyber threats. However, issues with multi-class intrusion detection include an imbalanced data set, poor accuracy for minority classes, and a lack of flexibility in handling complex real-world situations. To address these issues, we provide a hybrid framework that combines machine learning and deep learning methods to address these problems. The model uses a random forest classifier for anomaly detection after reducing dimensionality using an autoencoder. The Synthetic Minority Oversampling Technique (SMOTE) was used during processing to ensure equitable class representation and reduce class imbalance. A multi-class intrusion detection dataset tailored to healthcare applications was used to thoroughly test the suggested framework, which provides an impressive 99% accuracy rate. In addition to its excellent accuracy, the model addresses important issues in multi-class Intrusion detection by exhibiting remarkable precision for minority classes and consistent performance across all categories. These results highlight the framework\u27s effectiveness in providing dependable and effective normal detection solutions, which makes it ideal for implementation in crucial sectors like healthcare, their accuracy and data security are crucial
