Age-Stratified Differences in Morphological Connectivity Patterns in ASD: An sMRI and Machine Learning Approach

Purpose: Age biases have been identified as an essential factor in the diagnosis of ASD. The objective of this study was to compare the effect of different age groups in classifying ASD using morphological features (MF) and morphological connectivity features (MCF). Methods: The structural magnetic resonance imaging (sMRI) data for the study was obtained from the two publicly available databases, ABIDE-I and ABIDE-II. We considered three age groups, 6 to 11, 11 to 18, and 6 to 18, for our analysis. The sMRI data was pre-processed using a standard pipeline and was then parcellated into 148 different regions according to the Destrieux atlas. The area, thickness, volume, and mean curvature information was then extracted for each region which was used to create a total of 592 MF and 10,878 MCF for each subject. Significant features were identified using a statistical t-test (p<0.05) which was then used to train a random forest (RF) classifier. Results: The results of our study suggested that the performance of the 6 to 11 age group was the highest, followed by the 6 to 18 and 11 to 18 ages in both MF and MCF. Overall, the MCF with RF in the 6 to 11 age group performed better in the classification than the other groups and produced an accuracy, F1 score, recall, and precision of 75.8%, 83.1%, 86%, and 80.4%, respectively. Conclusion: Our study thus demonstrates that morphological connectivity and age-related diagnostic model could be an effective approach to discriminating ASD.Comment: 10 pages, 4 figures, 3 table

Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks

The limited dynamic range of commercial compact camera sensors results in an inaccurate representation of scenes with varying illumination conditions, adversely affecting image quality and subsequently limiting the performance of underlying image processing algorithms. Current state-of-the-art (SoTA) convolutional neural networks (CNN) are developed as post-processing techniques to independently recover under-/over-exposed images. However, when applied to images containing real-world degradations such as glare, high-beam, color bleeding with varying noise intensity, these algorithms amplify the degradations, further degrading image quality. We propose a lightweight two-stage image enhancement algorithm sequentially balancing illumination and noise removal using frequency priors for structural guidance to overcome these limitations. Furthermore, to ensure realistic image quality, we leverage the relationship between frequency and spatial domain properties of an image and propose a Fourier spectrum-based adversarial framework (AFNet) for consistent image enhancement under varying illumination conditions. While current formulations of image enhancement are envisioned as post-processing techniques, we examine if such an algorithm could be extended to integrate the functionality of the Image Signal Processing (ISP) pipeline within the camera sensor benefiting from RAW sensor data and lightweight CNN architecture. Based on quantitative and qualitative evaluations, we also examine the practicality and effects of image enhancement techniques on the performance of common perception tasks such as object detection and semantic segmentation in varying illumination conditions.Comment: Accepted in BMVC 202

Bot Detection in Social Networks Based on Multilayered Deep Learning Approach

With the swift rise of social networking sites, they have now come to hold tremendous influence in the daily lives of millions around the globe. The value of one’s social media profile and its reach has soared highly. This has invited the use of fake accounts, spammers and bots to spread content favourable to those who control them. Thus, in this project we propose using a machine learning approach to identify bots and distinguish them from genuine users. This is achieved by compiling activity and profile information of users on Twitter and subsequently using natural language processing and supervised machine learning to achieve the objective classification. Finally, we compare and analyse the efficiency and accuracy of different learning models in order to ascertain the best performing bot detection system

Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks

Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster’s trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% hike in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm

The majority of people in the modern biosphere struggle with depression as a result of the coronavirus pandemic&rsquo;s impact, which has adversely impacted mental health without warning. Even though the majority of individuals are still protected, it is crucial to check for post-corona virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally, if the post-symptoms are severe and go untreated, it might harm one&rsquo;s mental health. In order to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person&rsquo;s mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%

Study of soft classification approaches for identification of earthquake-induced liquefied soil

The existence of mixed pixels led to the development of several approaches for soft (or fuzzy) classification in which each pixel is allocated to all classes in varying proportions. However, while the proportions of each land cover within each pixel may be predicted, the spatial location of each land cover within each pixel is not. There exist many different potential techniques for sub-pixel mapping from remotely sensed imagery to identify specific class. The fuzzy-based possibilistic c-means (PCM), noise cluster (NC) and noise cluster with entropy (NCE) classifiers were applied to identify the Bhuj, India (2001), earthquake induced soil liquefaction and compared as soft computing approaches via supervised classification. The soil liquefaction identification was empirically investigated and compared with class-based sensor-independent (CBSI) spectral band ratio using Landsat-7 temporal images. It has been found that CBSI-based temporal indices yield the better results for identification of liquefied soil areas while it was easily separated with pre-earthquake existing water body in that area. The NCE classifier performed better for conventional temporal indices, while NC classifier performed better for soil liquefaction and PCM classifier performed better for water body identification with CBSI temporal indices