DeepDrive: A braking decision making approach using optimized GAN and Deep CNN for advanced driver assistance systems

Reduction of the number of traffic accidents is a vital requirement in many countries over the world. In these circumstances, the Human–Robot Interaction (HRI) mechanisms utilization is currently exposed as a possible solution to recompense human limits. It is crucial to create a braking decision-making model in order to produce the optimal decisions possible because many braking decision-making approaches are launched with minimal performance. An effective braking decision-making system, named Optimized Deep Drive decision model is developed for making braking decisions. The video frames are extracted and the segmentation process is done using a Generative Adversarial Network (GAN). GAN is trained using the newly developed optimization technique known as the Autoregressive Anti Corona Virus Optimization (ARACVO) algorithm. ARACVO is created by combining the Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaR) and Anti Corona Virus Optimization (ACVO) models. After retrieving the useful information for processing, the Deep Convolutional Neural Network (Deep CNN) is next used to decide whether to apply the brakes. The proposed approach improved performance by achieving maximum values of 0.911, 0.906, 0.924, and 0.933 for segmentation accuracy, accuracy, sensitivity, and specificity. © 2023 Elsevier Ltd

Feedback Artificial Tree-Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder

Autism spectrum disorder (ASD) is an umbrella term for a number of neurodevelopmental conditions with many heterogeneous behavioural indications. Recent medical imaging approaches use functional Magnetic Resonance Imaging (fMRI) for human recognition of the various neurological syndromes. However, these traditional techniques are time consuming and expensive. Thus, in this research, an optimization assisted deep learning technique, named Feedback Artificial Virus Optimization (FAVO)-based deep residual network (DRN), is developed. FAVO-based DRN is designed to incorporate the Feedback Artificial Tree (FAT) algorithm with Anti Corona Virus Optimization (ACVO). First, Region-Of-Interest extraction is carried out using thresholding techniques with nub region extraction completed using the proposed FAVO algorithm. ASD classification is then carried out using a DRN classifier. Evaluation of the proposal uses the ABIDE-1 and ABIDE-2 datasets. The developed FAVO algorithm attains better accuracy, sensitivity, and specificity of 0.9214, 0.9365, and 0.9142, respectively, by considering ABIDE-2 dataset. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Optimized speaker change detection approach for speaker segmentation towards speaker diarization based on deep learning

Speaker diarization is the partitioning of an audio source stream into homogeneous segments according to the speaker's identity. It can improve the readability of an automatic speech transcription by segmenting the audio stream into speaker turns and identifying the speaker's true identity when used in combination with speaker recognition systems. Generally, the automatic speaker diarization is done based on two phases, like the transformation of audio segments into feature representation and the clustering. In this paper, clustering along with a hybrid optimization technique is carried out for performing the speaker diarization. For that, the extracted features from the audio signal is processed under speech activity prediction in order to identify the speak segments. The diarization process is done by Deep Embedded Clustering (DEC) in which the constants are trained by the developed Fractional Anticorona Whale Optimization Algorithm (FrACWOA). The FrACWOA is a hybrid optimization technique, which is designed by adapting the concept of fractional theory, precaution behaviour of COVID-19 and hunting performance of whales. DEC performs the diarization, which concurrently learns the representation of features as well as cluster assignments with neural networks. Using a mapping from the information space to a lower-dimensional feature space, DEC repeatedly discovers the most effective solution for a clustering objective. On the basis of testing accuracy, diarization error, false discovery rate (FDR), false negative rate (FNR), and false positive rate (FPR) of 0.902, 0.627, 0.276, 0.117, and 0.118, respectively, the developed FrACWOA+DEC algorithm performed much better with six speakers using the EenaduPrathidwani dataset. Comparing the accuracy of the proposed method to existing approaches such as Active learning, DE+K-means, LSTM, MCGAN, ANN-ABC-LA, and ACWOA+DFC, the accuracy of the proposed method is 12.97%, 10.31%, 9.75%, 7.53%, 4.32%, and 2.106% higher when using 6 speakers. © 2022 Elsevier B.V.

Explaining the role and responsibilities of the National Anti-Coronavirus Headquarters in prevention and emergency response to pandemics in the workplace: a qualitative study on COVID-19 experience in Iran.

BACKGROUND: In recent years, the Coronavirus disease 2019 (COVID-19) have greatly affected the safety of life and the economy. Taking rapid measures to reduce these problems requires effective and efficient decisions by various departments and headquarters in a country. The purpose of this study was to investigate the role and responsibilities of the National Anti-Corona Headquarters (NACH) in the workplace during the pandemic. METHODS: This study was a qualitative study conducted using a triangulation approach. Data were obtained through semi-structured interviews with 18 participants with a purposive sampling technique as well as the review of related documents and records in response to the COVID-19 pandemic. The inductive and deductive approach was used for the content analysis of data in the Plan-Do-Check-Act (PDCA) model of the ISO45001 management system. RESULTS: Based on the results, four themes (plan, do, check, and act) were considered as the main domains. Subthemes include understanding the needs and expectations of interested parties; specific policy-making for organizations/workplaces; leadership and organizational commitment; addressing risks and opportunities; providing resources; competence of individuals and organizations; awareness; communication; information documentation; emergency response; monitoring, analyze, and evaluate performance; management review; non-compliance and corrective action; and improvement in pandemic control. CONCLUSION: To ensure the effectiveness and efficiency of organizations to deal with pandemics, the NACH must implement these responsibilities and play a pivotal role in responding to pandemics and using the participation of other government agencies and society. The findings of this study can be useful from national to local levels.

FACVO-DNFN: Deep learning-based feature fusion and Distributed Denial of Service attack detection in cloud computing

Cloud computing offers a broad range of resource pools for conserving a huge quantity of information. Due to the intrusion of attackers, the information that exists in the cloud is threatened. Distributed Denial of Service (DDoS) attack is the main reason for attacks in the cloud. In this study, a Fractional Anti Corona Virus Optimization-based Deep Neuro-Fuzzy Network (FACVO-based DNFN) is devised for detecting DDoS in the cloud. The production of log files, feature fusion, data augmentation, and DDoS attack detection is the processing stages involved in this phase of the DDoS attack detection process. The feature fusion is carried out by RV coefficient and Deep Quantum Neural Network (Deep QNN), and the data augmentation is performed. Then, the Anti Corona Virus Optimization (ACVO) method and Fractional Calculus (FC) are both incorporated to create the FACVO algorithm. The DNFN is trained by the created FACVO algorithm, which identifies the DDoS attack. The proposed approach achieved testing accuracy, TPR, TNR, and precision values of 0.9304, 0.9088, 0.9293, and 0.8745 for using the NSL-KDD dataset without attack, and 0.9200, 0.8991, 0.9015, and 0.8648 for using the BoT-IoT dataset without attack. [ FROM AUTHOR]

ExpACVO-Hybrid Deep learning: Exponential Anti Corona Virus Optimization enabled Hybrid Deep learning for tongue image segmentation towards diabetes mellitus detection.

A metabolic disease known as diabetes mellitus (DM) is primarily brought on by an increase in blood sugar levels. On the other hand, DM and the complications it causes, such as diabetic Retinopathy (DR), will quickly emerge as one of the major health challenges of the twenty-first century. This indicates a huge economic burden on health-related authorities and governments. The detection of DM in the earlier stage can lead to early diagnosis and a considerable drop in mortality. Therefore, in order to detect DM at an early stage, an efficient detection system having the ability to detect DM is required. An effective classification method, named Exponential Anti Corona Virus Optimization (ExpACVO) is devised in this research work for Diabetes Mellitus (DM) detection using tongue images. Here, the UNet-Conditional Random Field-Recurrent Neural Network (UNet-CRF-RNN) is used to segment the images, and the proposed ExpACVO algorithm is used to train the UNet-CRF-RNN. Deep Q Network (DQN) classifier is used for DM detection, and the proposed ExpACVO is used for DQN training. The proposed ExpACVO algorithm is a newly created formula that combines Anti Corona Virus Optimization(ACVO) with Exponential Weighted Moving Average (EWMA). With maximum testing accuracy, sensitivity, and specificity values of 0.932, 0.950, and 0.914, respectively, the developed technique thus achieved improved performance.

FACVO-DNFN: Deep learning-based feature fusion and Distributed Denial of Service attack detection in cloud computing

Cloud computing offers a broad range of resource pools for conserving a huge quantity of information. Due to the intrusion of attackers, the information that exists in the cloud is threatened. Distributed Denial of Service (DDoS) attack is the main reason for attacks in the cloud. In this study, a Fractional Anti Corona Virus Optimization-based Deep Neuro-Fuzzy Network (FACVO-based DNFN) is devised for detecting DDoS in the cloud. The production of log files, feature fusion, data augmentation, and DDoS attack detection is the processing stages involved in this phase of the DDoS attack detection process. The feature fusion is carried out by RV coefficient and Deep Quantum Neural Network (Deep QNN), and the data augmentation is performed. Then, the Anti Corona Virus Optimization (ACVO) method and Fractional Calculus (FC) are both incorporated to create the FACVO algorithm. The DNFN is trained by the created FACVO algorithm, which identifies the DDoS attack. The proposed approach achieved testing accuracy, TPR, TNR, and precision values of 0.9304, 0.9088, 0.9293, and 0.8745 for using the NSL-KDD dataset without attack, and 0.9200, 0.8991, 0.9015, and 0.8648 for using the BoT-IoT dataset without attack.

Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things

The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti-corona virus-Henry gas solubility optimization-based deep maxout network (ACV-HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV-HGSO is designed by incorporating anti-corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi-objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension-reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively. © 2022 John Wiley & Sons, Ltd.

Feedback Artificial Tree‐Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder

Autism spectrum disorder (ASD) is an umbrella term for a number of neurodevelopmental conditions with many heterogeneous behavioural indications. Recent medical imaging approaches use functional Magnetic Resonance Imaging (fMRI) for human recognition of the various neurological syndromes. However, these traditional techniques are time consuming and expensive. Thus, in this research, an optimization assisted deep learning technique, named Feedback Artificial Virus Optimization (FAVO)‐based deep residual network (DRN), is developed. FAVO‐based DRN is designed to incorporate the Feedback Artificial Tree (FAT) algorithm with Anti Corona Virus Optimization (ACVO). First, Region‐Of‐Interest extraction is carried out using thresholding techniques with nub region extraction completed using the proposed FAVO algorithm. ASD classification is then carried out using a DRN classifier. Evaluation of the proposal uses the ABIDE‐1 and ABIDE‐2 datasets. The developed FAVO algorithm attains better accuracy, sensitivity, and specificity of 0.9214, 0.9365, and 0.9142, respectively, by considering ABIDE‐2 dataset. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

How People Evaluate Anti-Corona Measures for Their Social Spheres: Attitude, Subjective Norm, and Perceived Behavioral Control.

Restrictions on outdoor activities, tips for hygiene, and tips for mental health are among the most common initiatives to counter the COVID-19 pandemic. These measures aim to protect people's health and, at the same time, impact their social lives. So far, it is little known how people evaluate those anti-Corona measures with regard to their social spheres (close family, wider family and friends, colleagues, and society). Furthermore, it is plausible that the subjective evaluation of attitudinal objects and especially severe events, like the COVID-19 pandemic and the related counter-measures, is multidimensional. Against this background, we combine the social spheres with the elements of the Theory of Planned Behavior. On the methodological basis of the Means-End Theory of Complex Cognitive Structures, we determine the perceived relevance and quality of the attitude, subjective norm, perceived behavioral control, and social spheres regarding anti-Corona measures. Furthermore, the applied methodology allows the deduction of norm strategies to define the priority of securing or increasing the effectiveness of elements of anti-Corona measures. Based on the answers of 663 participants, we found that the protection from COVID-19 and its consequences (attitude) are more important to people than the practicability of anti-Corona measures in their social lives (perceived behavioral control), which, again, has a higher subjective relevance than the willingness to fulfill the expectations of others (subjective norm). Additionally, people distinguish between their close family (higher subjective relevance) and their other social spheres (lower subjective relevance). The people attribute the highest quality to the tips on hygiene, followed by the restrictions on outdoor activities and the tips for mental health. The protection and practicability of the anti-Corona measures have higher quality ratings than the willingness to fulfill the expectations of others. Based on the norm strategies, policymakers should secure the effectiveness of the current anti-Corona measures with a high priority by focusing on the protection and practicability with regard to close and wider family and friends. Increasing the effectiveness of the protection and practicability of anti-Corona measures in work and society also has a high priority. Focusing on the subjective norm should be of lower priority.