Physical activity based classification of serious mental illness group participants in the UK Biobank using ensemble dense neural networks

Serious Mental Illnesses (SMIs) including schizophrenia and bipolar disorder are long term conditions which place major burdens on health and social care services. Locomotor activity is altered in many cases of SMI, and so in the long term wearable activity trackers could potentially aid in the early detection of SMI relapse, allowing early and targeted intervention. To move towards this goal, in this paper we use accelerometer activity tracking data collected from the UK Biobank to classify people as being either in a self-reported SMI group or an age and gender matched control group. Using an ensemble dense neural network algorithm we exploited hourly and average derived features from the wearable activity data and the created model obtained an accuracy of 91.3%

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets1,2. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and Pneumonia from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 90%, 94.3%, and 96.8% for the VGG16, ResNet50, and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a CycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we implemented a gradient class activation mapping technique to highlight the regions of the input image that are important for predictions. Additionally, these visualizations can be used to monitor the affected lung regions during disease progression and severity stages

Evaluation of supervised classification algorithms for human activity recognition with inertial sensors

The main aim of this work is to compare the performance of different algorithms for human activity recognition by extracting various statistical time domain and frequency domain features from the inertial sensor data. Our results show that Support Vector Machines with quadratic kernel classifier (accuracy: 93.5%) and Ensemble classifier with bagging and boosting (accuracy: 94.6%) outperforms other known activity classification algorithms. A parallel coordinate plot based on visualization of features is used to identify useful features or predictors for separating classes. This enabled exclusion of features that contribute least to classification accuracy in a multi-sensor system (five in our case), made the classifier lightweight in terms of number of useful features, training time and computational load and lends itself to real-time implementation

Explainable Droplet Recognition System for Precision Sprayer Applications

AI-driven detection systems are playing an increasingly important role in the advancement of precision agriculture. In this paper, we have implemented a transfer learning pipeline for water droplet detection with the intent to develop quantifiable and real-time detection of post-spray areas for precision spraying applications. The object detection pipeline effectively identified multiple features for water droplet detection from the three curated datasets. We have used two pre-trained convolutional backbones as the feature extractor and achieved an overall detection mean average precision across the three curated datasets of 0.409 and 0.277 for the ResNet50, and MobileNetV3-Large backbones respectively. Additionally, for visual explanations and interpretation, we implemented EigenCAM class activation mapping techniques to highlight the regions of the input images that are important for predictions

An Explainable AI-based Intrusion Detection System for DNS over HTTPS (DoH) Attacks

Over the past few years, Domain Name Service (DNS) remained a prime target for hackers as it enables them to gain first entry into networks and gain access to data for exfiltration. Although the DNS over HTTPS (DoH) protocol has desirable properties for internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining a secure network, in this paper, we have implemented an explainable AI solution using a novel machine learning framework. We have used the publicly available CIRA-CIC-DoHBrw-2020 dataset for developing an accurate solution to detect and classify the DNS over HTTPS attacks. Our proposed balanced and stacked Random Forest achieved very high precision (99.91\%), recall (99.92\%) and F1 score (99.91\%) for the classification task at hand. Using explainable AI methods, we have additionally highlighted the underlying feature contributions in an attempt to provide transparent and explainable results from the model

An explainable AI-based intrusion detection system for DNS over HTTPS (DoH) attacks

Over the past few years, Domain Name Service (DNS) remained a prime target for hackers as it enables them to gain first entry into networks and gain access to data for exfiltration. Although the DNS over HTTPS (DoH) protocol has desirable properties for internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining a secure network, in this paper, we have implemented an explainable AI solution using a novel machine learning framework. We have used the publicly available CIRA-CIC-DoHBrw-2020 dataset for developing an accurate solution to detect and classify the DNS over HTTPS attacks. Our proposed balanced and stacked Random Forest achieved very high precision (99.91%), recall (99.92%) and F1 score (99.91%) for the classification task at hand. Using explainable AI methods, we have additionally highlighted the underlying feature contributions in an attempt to provide transparent and explainable results from the model

An Agricultural Precision Sprayer Deposit Identification System

Data-driven Artificial Intelligence systems are playing an increasingly significant role in the advancement of precision agriculture. Currently, precision sprayers lack fully automated methods to evaluate the effectiveness of their operation, e.g. whether spray has landed on target weeds. In this paper, using an agricultural spot spraying system images were collected from an RGB camera to locate spray deposits on weeds or lettuces. We present an interpretable deep learning pipeline to identify spray deposits on lettuces and weeds without using existing methods such as tracers or water-sensitive papers. We implement a novel stratification and sampling methodology to improve results from a baseline. Using a binary classification head after transfer learning networks, spray deposits are identified with over 90% Area Under the Receiver Operating Characteristic (AUROC). This work offers a data-driven approach for an automated evaluation methodology for the effectiveness of precision sprayers

Design and implementation of a convolutional neural network on an edge computing smartphone for human activity recognition

Edge computing aims to integrate computing into everyday settings, enabling the system to be context-aware and private to the user. With the increasing success and popularity of deep learning methods, there is an increased demand to leverage these techniques in mobile and wearable computing scenarios. In this paper, we present an assessment of a deep human activity recognition system’s memory and execution time requirements, when implemented on a mid-range smartphone class hardware and the memory implications for embedded hardware. This paper presents the design of a convolutional neural network (CNN) in the context of human activity recognition scenario. Here, layers of CNN automate the feature learning and the influence of various hyper-parameters such as the number of filters and filter size on the performance of CNN. The proposed CNN showed increased robustness with better capability of detecting activities with temporal dependence compared to models using statistical machine learning techniques. The model obtained an accuracy of 96.4% in a five-class static and dynamic activity recognition scenario. We calculated the proposed model memory consumption and execution time requirements needed for using it on a mid-range smartphone. Per-channel quantization of weights and per-layer quantization of activation to 8-bits of precision post-training produces classification accuracy within 2% of floating-point networks for dense, convolutional neural network architecture. Almost all the size and execution time reduction in the optimized model was achieved due to weight quantization. We achieved more than four times reduction in model size when optimized to 8-bit, which ensured a feasible model capable of fast on-device inference

Evaluating the Use of Interpretable Quantized Convolutional Neural Networks for Resource-Constrained Deployment

The deployment of Neural Networks on resource-constrained devices for object classification and detection has led to the adoption of network compression methods, such as Quantization. However, the interpretation and comparison of Quantized Neural Networks with their Non-Quantized counterparts remains inadequately explored. To bridge this gap, we propose a novel Quantization Aware eXplainable Artificial Intelligence (XAI) pipeline to effectively compare Quantized and Non-Quantized Convolutional Neural Networks (CNNs). Our pipeline leverages Class Activation Maps (CAMs) to identify differences in activation patterns between Quantized and Non-Quantized. Through the application of Root Mean Squared Error, a subset from the top 5% scoring Quantized and Non-Quantized CAMs is generated, highlighting regions of dissimilarity for further analysis. We conduct a comprehensive comparison of activations from both Quantized and Non-Quantized CNNs, using Entropy, Standard Deviation, Sparsity metric s, and activation histograms. The ImageNet dataset is utilized for network evaluation, with CAM effectiveness assessed through Deletion, Insertion, and Weakly Supervised Object Localization (WSOL). Our findings demonstrate that Quantized CNNs exhibit higher performance in WSOL and show promising potential for real-time deployment on resource-constrained devices

An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks

A Network Intrusion Detection System is a critical component of every internet-connected system due to likely attacks from both external and internal sources. Such Security systems are used to detect network born attacks such as flooding, denial of service attacks, malware, and twin-evil intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in 5G and IoT network. We evaluated the algorithm with the benchmark Aegean Wi-Fi Intrusion dataset. Our results showed an excellent performance with an overall detection accuracy of 99.9% for Flooding, Impersonation and Injection type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm