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This paper proposed a Constructive Rapid Application Development Model based Software Cost Estimation Technique (CORADMO based SCET) for accurate software cost estimation. The data requirements, cost drivers, constraints and priorities are given as an input to the Fuzzy Inference System (FIS). The processed output such as effort, time and cost for nominal plan, shortest schedule plan, and least cost plan are computed in the Fuzzy Inference System (FIS). Further to reduce the effort time and cost means, the output optimized by Dragon fly Whale Optimization (DWO)which provides the best estimated effort, time and cost as an output for software development. The proposed CORADMO based SCET model is evaluated by NASA 93 dataset using MATLAB. The performance of the CORADMO based SCET approach is assessed in terms of Mean Magnitude of Relative Error, Pred (25%), and Magnitude of Relative Error attains the values of 98.77%, 92.55%, and 93.45% respectively. Finally, the CORADMO based SCET model justifies the suitability of Dragon fly Whale Optimization with the proposed fuzzy logic.

Globally, cardiovascular disease kills more than 500000 people every year, thus becoming the primary reason for death. Nevertheless, cardiovascular health monitoring is essential for accurate analysis and therapy of heart disease. In this work, a novel deep learning-based StrIppeD NAS-Network (SID-NASNet) for arrhythmia categorization into octa-classes with electrocardiogram (ECG) signals is presented. First, the ECG signals are recorded in real time using 12-lead electrodes. Then, the Discrete Wavelet Transform (DWT) is used to denoise the signals to reduce repetition and increase resilience. The noise-free ECG signals are fed into a K-means clustering algorithm to group ECG signal segments into a set number of clusters to identify patterns that may indicate heart abnormalities. Subsequently, the deep learning-based NASNet with Stripped convolutional layers is used to detect ECG irregularities of arrhythmia. Each sample point is examined for its local fractal dimension before extracting the heartbeat waveforms within a predetermined window length. A bio-inspired Dingo Optimization (DO) algorithm is used in the SID-NASNet to normalize the parameters to improve the efficiency of the network with low network complexity. The efficiency of the proposed SID-NASNet is assessed with specificity, accuracy, precision, F1 score and recall based on the MIT-BIH arrhythmia dataset. From the test results, the proposed SID-NASNet achieves an accuracy of 98.22% for effective categorization of ECG signals. The proposed SID-NASNet improves the overall accuracy of 1.24%, 3.76%, 1.87%, and 0.22% better than ECG-NET, Deep Learning (DL)-based GAN, 1D-CNN, and GAN-Long-Short Term Memory (LSTM), respectively.

Energy harvesting and Internet of Things (IoT) technology is being integrated with home appliances. IoT devices could harvest energy from alternative energy sources to convert the available energy from home appliances into electrical energy. Smart home energy management systems face isolated scheduling horizons and unreliable inputs. In this research, an IoT Heterogeneous Energy Harvesting (IHEH) technique has been proposed for residential home energy management to operate the smart home appliances which consists of four layers, namely, the Energy Harvesting Layer (EHL), the Control and Sensing Layer (CSL), the Application Layer (AL), and the Information Processing Layer (IPL). The proposed technique keeps track of the distribution of several types of energy, including thermal, piezoelectric, and light energy. The overall efficiency of the proposed method is 90% per day. The proposed IHEH technique yields more power and increases the lifetime of batteries.

As digital products communicate over public networks, digital image security becomes essential. Despite advancements in encryption technology, protecting data from secure images remains a complex computational issue that necessitates using an encrypted environment to protect data transmitted from devices over networks. Encryption is critical for protecting sensitive information, especially images, from unauthorized access or failure. Image encryption is distinct from text encryption. Images contain extensive data with high redundancy and a high correlation between nearby pixels, making them difficult to process with traditional technology. In recent decades, chaos maps have become popular in the crypto community. This work proposes a new encryption technology based on chaotic map substitution boxes (S-box) and cellular automata (CA) to address the frequent challenges in chaotic encryption methods. To address the inadequate randomness provided by the 1D chaotic map, this work presented a 4D memristive hyperchaos with a more excellent chaotic range, increased uncertainty, and ergodicity as an alternative to the software-based approach, which is vulnerable and offers low throughput. The suggested encryption technique was implemented on an Intel Cyclone IV EP4CE115F29C7 FPGA. It is less prone to tampering and has a better throughput. This suggested encryption scheme employs 1384 LEs. In comparison to traditional algorithms, the suggested Number of Pixels Change Rate (NPCR) (99.9706%), unified averaged changed intensity (UACI) (33.3956%), and information entropy (IE) (7.984) accomplish more effective. Statistical studies such as IE, histogram, correlation, peak signal-to-noise ratio (PSNR), differential analysis such as NPCR and UACI, and noise attack analyses are employed to validate the suggested encryption design. Regarding security, the suggested hybrid method outperforms conventional algorithms while protecting image quality.

Introduction Opioids and imaging are considered low-value care for most people with low back pain. Yet around one in three people presenting to the emergency department (ED) will receive imaging, and two in three will receive an opioid. NUDG-ED aims to determine the effectiveness of two different behavioural ‘nudge’ interventions on low-value care for ED patients with low back pain.Methods and analysis NUDG-ED is a 2×2 factorial, open-label, before-after, cluster randomised controlled trial. The trial includes 8 ED sites in Sydney, Australia. Participants will be ED clinicians who manage back pain, and patients who are 18 years or over presenting to ED with musculoskeletal back pain. EDs will be randomly assigned to receive (i) patient nudges, (ii) clinician nudges, (iii) both interventions or (iv) no nudge control. The primary outcome will be the proportion of encounters in ED for musculoskeletal back pain where a person received a non-indicated lumbar imaging test, an opioid at discharge or both. We will require 2416 encounters over a 9-month study period (3-month before period and 6-month after period) to detect an absolute difference of 10% in use of low-value care due to either nudge, with 80% power, alpha set at 0.05 and assuming an intra-class correlation coefficient of 0.10, and an intraperiod correlation of 0.09. Patient-reported outcome measures will be collected in a subsample of patients (n≥456) 1 week after their initial ED visit. To estimate effects, we will use a multilevel regression model, with a random effect for cluster and patient, a fixed effect indicating the group assignment of each cluster and a fixed effect of time.Ethics and dissemination This study has ethical approval from Southwestern Sydney Local Health District Human Research Ethics Committee (2023/ETH00472). We will disseminate the results of this trial via media, presenting at conferences and scientific publications.Trial registration number ACTRN12623001000695.

Background Rapid detection of intracranial arterial occlusion in patients with ischemic stroke is important to facilitate timely reperfusion therapy. We compared the diagnostic accuracy of neurologists and radiologists against RapidAI (iSchema View, Menlo Park, CA) software for occlusion detection. Methods Adult patients who presented to a single comprehensive stroke center over a 5‐month interval with clinical suspicion of ischemic stroke and who underwent multimodality imaging with RapidAI interpretation were included. There were 8 assessors: 1 radiologist, 5 neurologists, and 2 radiology trainees. The reference standard was large‐vessel occlusion (LVO) or medium‐vessel occlusion (MVO) diagnosed by a panel of 4 interventional neuroradiologists. Positive likelihood ratio (LR) and negative LR were used to indicate how well readers correctly classified the presence of intracranial occlusions compared with the reference standard. The positive LR and negative LR for each reader were plotted on an LR graph using RapidAI LRs as comparator. Results The assessors read scans from 500 patients (49.6% men). The positive LR of RapidAI for detection of LVO was 8.49 (95% CI, 5.75–12.54), and the negative LR was 0.41 (95% CI, 0.28–0.58). The positive LR for LVO or MVO for RapidAI was 5.0 (95% CI, 3.28–7.63), and the negative LR was 0.66 (95% CI, 0.56−0.79). Sensitivity for LVO (0.65–0.96) and for LVO or MVO (0.62–0.94) was higher for all readers compared with RapidAI (0.62 and 0.39, respectively). Six of 8 readers had superior specificity to RapidAI for LVO (0.75–0.98 versus 0.93) and LVO or MVO (0.55–0.95 versus 0.92). Conclusions Experienced readers of acute stroke imaging can identify LVOs and MVOs with higher accuracy than RapidAI software in a real‐world setting. The negative LR of RapidAI software was not sufficient to rule out LVO or MVO.