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A novel framework for abnormal risk classification over fetal nuchal translucency using Adaptive Stochastic Gradient Descent Algorithm
No full textIn most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy. Birth defects and congenital abnormalities are certain fetal abnormalities. Fetal abnormalities have become common in several industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters like accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through suggested technique is of 98.642.%
A counter-eavesdropping technique for optimized privacy of wireless industrial IoT communications
No full textThe Industrial Internet of Things (IIoTs) is a key component of the fourth industrial revolution (Industry 4.0) which is faced with privacy issues as the scale and sensitivity of user and system data constantly increases. Eavesdropping attack is one of such privacy issues of the IIoT system especially when the number of transmitting antennas is increased. Thus, the focus of this paper is on establishing efficient privacy in an IIoT-MIMOME communications scenario. To achieve this, a closed-form derivation for asymptotic regularized prompt privacy rate is first formulated for IIoT network system. Then, the study further examines the design of optimal jamming parameters by proposing a model referred as Optimal Counter-Eavesdropping Channel Approximation (OPCECA) technique for tackling eavesdropping attack in IIoT. The simulated performance of the proposed model clearly shows that provided that the channel coherence time is less than two times number of transmitting nodes, a high privacy precision is achieved even without deploying any artificial noise
Necrosadism: exploring the sexual component of post-mortem mutilation of homicide victims
No full textIt is only relatively recently that necrophilic behaviour has been recognised as diverse in nature, the importance of which is directly linked to correct treatment options. The distinct paraphilic disorder of necrosadism, the sexual arousal derived from the mutilation of corpses, is presented in a case here to clarify existing definitions of the disorder, describe its manifestation in cases of homicide, and explore the psychology driving the behaviour. In so doing, it is hoped that the behaviour is more succinctly defined and better understood so appropriate and tailored treatment options for the disorder can be discussed and formulated
Hash-based deep learning approach for remote sensing satellite imagery detection
No full textShip detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof
Applying the principles of health promotion in nursing practice
No full textHealth promotion is a relatively new concept that is grounded in the definition of health proposed in 1946 by the World Health Organization. The thinking behind health promotion departs from the biomedical model of health and considers how biological, psychological and social factors interact to affect the health and health outcomes of individuals, communities and population groups. An accurate and in-depth understanding of what health means to people and how health is experienced enables healthcare professionals, planners and policymakers to develop and deliver health promotion interventions that prevent suboptimal health and address health inequalities. This article describes the concepts, frameworks and models that underpin health promotion and discusses the different types of health promotion interventions that can be applied in nursing practice
A tale of two Peters: an analysis of the life of Peter Green using collaborative/community autoethnography and digital team ethnography
No full textThe purpose of this analysis was to mirror the late guitarist Peter Green’s life experiences through insights from Andrew Voyce, who recovered from mental illness, and expertise from Peter Bryngelsson, a Swedish professional musician and author.The authors used a mixed method of collaborative autoethnography, psychobiography and digital team ethnography.Despite having not previously attracted academic interest, Peter Green’s experiences of mental health problems and his return to recording and performance provide a rich data source when mirrored and compared to the lives and experiences of Andrew Voyce and Peter Bryngelsson.The main limitation of this piece of work is that Peter Green died in 2020. During the process of writing the authors have had to follow different, mostly unacademic, sources which have described various parts of Peter Green’s life. The authors have given examples and drawn conclusions from their own lives as well as from academic sources, which they have found appropriate.Both Andrew Voyce’s and Peter Bryngelsson’s stories would be helpful when it comes to a deeper understanding as to why Peter Green ‘took a left turn’ i.e., turned his back on an accepted life style.Acid casualty is a problem connected to both mental distress and to the music industry. Peter Bryngelsson’s story tells us that one can remain sane and drug free and still be an influential and creative musician.The analysis has brought together two stories of mental distress in combination with insights
SVM based generative adverserial networks for federated learning and edge computing attack model and outpoising
No full textMachine learning algorithms are prone to attacks: An attackers can use the malicious nodes to attack the training dataset to manipulate the process of learning and reduce the efficiency of the algorithm working performance. Optimal poisoning attacks have already been proposed to evaluate worst case scenarios, modelling attacks as a bilevel optimization problem. Solving these problems is computationally demanding and has limited applicability for some models such as deep networks. In this paper we introduce a novel generative model to craft systematic poisoning attacks against machine learning classifiers generating adversarial training examples, i.e. samples that look like genuine data points but that reduce the accuracy of the classifier in the process of training process. The proposed system have 3 components of Generative Adverserial networks (GAN) generator, discriminator, and the target classifier. The proposed system allows to detect the vulnerability easy and it can be found as similar as realistic attacks to detect the area where the underlying data distribution have more possibility of poising attack which cause vulnerability to the network. Our experimentation, proves the claim our that the proposed model is effective on compromising the classifiers uses the machine learning algorithms and also deep learning networks
Efficient Dynamic Phishing Safeguard System Using Neural Boost Phishing Protection
No full textThe instance of privacy and security have reached the point where they cannot be ignored. There has been a rise in data breaches and fraud, particularly in banks, healthcare, and government sectors. In today's world, many organizations offer their security specialists bug report programs that help them find the flaws in the applications. The breach of data on its own doesn't constitute a threat or attack. Cyber-attacks allow cyberpunks to gain access to machines and networks and steal financial data and esoteric information as a result of a data breach. In this context, this paper proposes an innovative approach to help users to avoid online subterfuge by implementing a Dynamic Phishing Safeguard System (DPSS) using neural boost phishing protection algorithm that focuses on phishing, fraud, and optimizes the problem of data breaches. Dynamic phishing safeguard utilizes 30 different features to predict whether or not a website is a phishing website. In addition, the neural boost phishing protection algorithm uses an Anti-Phishing Neural Algorithm (APNA) and an Anti-Phishing Boosting Algorithm (APBA) to generate output that is mapped to various other components, such as IP finder, geolocation, and location mapper, in order to pinpoint the location of vulnerable sites that the user can view, which makes the system more secure. The system also offers a website blocker, and a tracker auditor to give the user the authority to control the system.Based on the results, the anti-phishing neural algorithm achieved an accuracy level of 97.10%, while the anti-phishing boosting algorithm yielded 97.82%. According to the evaluation results, Dynamic phishing safeguard systems tend to perform better than other models in terms of uniform resource locator detection and security
Using medical education as a tool to train doctors as social innovators
No full textCurrent medical education prepares doctors to diagnose, assess and treat individual patients yet lacks the expectation to be responsible for the care of the wider community. Learning the skills to recognise and redress the social determinants of health are increasingly being recognised as an essential part of medical education.Objectives: The goal of this research was (1) to investigate how medical education can be leveraged to reduce health inequalities through the role and practice of doctors and (2) to elucidate how key innovations in medical education are a necessity that can support doctors as ‘change agents.’ Methods: Two international multidisciplinary roundtable focus groups with 23 healthcare leaders from various backgrounds were facilitated. The discussions were audio recorded, transcribed and then thematically analysed with the qualitative analysis software QDA Miner.Results: Eight themes emerged: (1) Social innovation training in medical education; (2) Linking community working with social innovation; (3) Future curricula development; (4) Settings, context, environment and leaving the classroom; (5) Developing links with third sector organisations and community, including low-income and middle-income countries; (6) Including learners’ perspectives and lived experience; (7) Medical roles are political and need political support and (8) The need to address power imbalances and impact of discrimination.Conclusions: Medical education needs to fundamentally widen its focus from the individual doctor–patient relationship to the doctor–community relationship. Doctors’ training needs to help them become social innovators who can balance interventions with prevention, promote good health on a community and societal scale and tailor their treatments to the individuals’ contexts