Diptiben Ghelani

and 1 more

Obesity is reaching epidemic proportions worldwide with profound impact on health resulting in reduced quality of life, early death. Deposition of excess fatty acids (FAs) into fat cells in the form of triglycerides (TGs) is the biochemical basis of obesity, thus any imbalance in food intake and energy utilization may result in obesity. This homeostasis is complex and is regulated by a host of metabolic and endocrine factors which are poorly understood. Obesity contributes to pathologies, such as the metabolic syndrome (MetS), cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), hypertension, endothelial dysfunction [ED] and testosterone deficiency (hypogonadism). An increases in the prevalence of overweight (body mass index (BMI) 25-29.9 kg m−2) and obesity (BMI ≥ 30 kg m−2) in adult men by more than 25% in the last 8 years according to WHO estimates. Overweight and moderate obesity is predominantly associated with reductions in total testosterone; whereas, free testosterone levels remain within the reference range, especially in younger men. Reductions in total testosterone levels are largely a consequence of reductions in sex hormone binding globulin (SHBG) due to obesity-associated hyperinsulinemia. Glucagon-like peptide-1 receptor agonists (GLP-1 RA) are used for weight loss and insulin dose reduction in obese insulin-using type 2 diabetic patients. A plausible mechanism by which GLP-1 RA may induce weight loss is by suppressing appetite signalling in the brain and increasing satiety, leading to a reduced food intake [9, 10]. GLP-1 receptors are present in the central nervous system suggesting direct actions of GLP-1 in the brain [11]. GLP-1 infusions can enhance satiety and reduce energy intake in type 2 diabetes patients [12]. Furthermore, GLP-1 RA attenuates binge eating in obese patients [13], suggesting a role of GLP-1 RA in certain eating types. A recent systematic review and meta-analysis including 2.8 million people and 270 000 deaths reported increased overall mortality only in those with extreme obesity (BMI > 35 kg m−2, hazard ratio (HR) 1.29, 95% confidence interval (CI) 1.18-1.41), but not in grade 1 obesity (BMI
There is a wealth of information security guidance available in academic and practitioner literature. Although other tactics such as deterrence, deception, detection, and reaction are possible, most of the research focuses on how to prevent security threats using technological countermeasures. The findings of a qualitative study conducted in Korea to determine how businesses use security techniques to protect their information systems are presented in this article. The results show a deeply ingrained preventative mindset, driven by a desire to ensure the availability of technology and services and a general lack of awareness of enterprise security concerns. While other tactics were evident, they were also preventative measures. The article lays out a research agenda for deploying multiple strategies across an enterprise, focusing on how to combine, balance, and optimize systems. This research looked at various topics, including information security and areas where security strategy is likely to be discussed, such as military sources. There are nine security strategies identified. A qualitative focus group approach is used to determine how these security strategies are used in organizations. In focus groups, security managers from eight organizations were asked to discuss their organizations' security strategies. According to the findings, many organizations use a preventive approach to keep technology services available. Some of the other identified methods were used to support the prevention strategy on an operational level.

Diptiben Ghelani

and 2 more

We live in a time when data security has become a significant concern. Cyber services are the most enjoyable and time-saving aspects of one's life. On the other hand, people save their data in the cloud, handled by the cyber. In this case, cyber-security is quite vital. This is an open security challenge because many intruders can attack the data and hack the user's details through the server. If we look around, we will see a lot of cases involving cyber-crime. The security of our cloud-based datasets has become a serious concern. Our research will include data security, including intruder detection that can happen anywhere on the planet. Protecting data from intruders has become critical, and intruder detection should be the essential key to identifying. How will we know who is stealing the data that has been secured using biometric security, fingerprints, passwords, OTPs, and other methods if we don't know who the intruder is? Intruder detection has become increasingly important, particularly on mobile objects such as aeroplanes and ships. We can only find a solution if we understand the problem. We employ machine learning, biometric recognition, data learning, and hybrid approaches to avoid this. These will be the system's handles, and they will help secure data from intruders by utilizing the best optimization techniques to obtain precise data. We proposed a banking system model in which biometric impressions and digital signatures are used to enable every transaction by a bank's customer. This proposal recommends that the Smart Online Banking System (SOBS) be made more secure by employing biometric prints, which decreases the number of threats that an invader may pose.
Department of Computer Engineering, Gujrat Technological College, Ahmedabad, IndiaEmail address:Shezi1131@gmail.comTo cite this article:Diptiben Ghelni. Deep Learning and Artificial Intelligence Framework to Improve the Cyber Security. American Journal of Artificial Intelligence . Vol. x, No. x, 2022, pp. x-x. Abstract: Deep learning derived from an artificial neural network (ANN), is one of the essential technologies for today’s intelligent cyber security systems or policies. The benefits and drawbacks of using artificial intelligence (AI) in cyber risk analytics to improve organizational resilience and better comprehend cyber risk. Multilayer perceptron, convolutional neural network, recurrent neural network or long short-term memory, self-organizing map, auto-encoder, restricted Boltzmann machine, deep belief networks, generative adversarial network, deep transfer learning, and deep reinforcement learning, as well as their ensembles and hybrid approaches, can be used to tackle the diverse cyber security issues intelligently. The backpropagation algorithm’s ultimate goal is to correctly maximize the network weights to translate the inputs to the intended outputs. During the training phase, several optimization approaches such as Stochastic Gradient Descent (SGD), Limited Memory BFGS (L-BFGS), and Adaptive Moment Estimation (Adam) are applied. These neural networks may be utilized to handle a variety of cybersecurity problems. MLP-based networks are used to construct an intrusion detection model, malware analysis, security threat analysis, identify malicious botnet traffic, and build trustworthy IoT systems. MLP is sensitive to feature scaling and requires tuning a variety of hyperparameters such as the number of hidden layers, neurons, and iterations, which might make solving a complicated security model computationally costly.Keywords: Cyber Security, Artificial Intelligence, Deep Learning, Internet of Things1. IntroductionIndustry 4.0, an IoT phrase coined in 1999, is built on the Internet of Things (IoT) technology, providing the first glimpse of what an IoT-based ecosystem would look like in the future. CPS refers to the interdisciplinary and complex characteristics of intelligent systems constructed and relies on the interplay of physical and computational components. CPS theory evolved from control theory and control systems engineering. It focuses on the connectivity of physical features and the utilization of sophisticated software entities to create new network and system capabilities. CPSs connect biological and engineering systems, bridging the cyber and physical worlds [1].On the other hand, IoT theory is based on computer science and Internet technologies, and it focuses primarily on the interconnection, interoperability, and integration of physical components on the Internet. This integration effort is expected to lead to advances such as IoT automation of CPSs as the IoT industry matures over the next decade. CPS systems and automated CPSs guide trained employees in production situations in real-time. In this context, we look at how such systems enable artificial intelligence (AI) breakthroughs in real-time processing, sensing, and actuation across these new systems and give cyber structure system analysis capabilities. As a result, we’ll concentrate on artificial intelligence, which is a notion that encompasses both the cyber-physical and social components of the hazards associated with new technology deployment [2].There are two research aims in this study. To begin, we provide an up-to-date summary of current and emerging cyber risk analytics breakthroughs. This incorporates current standards into a new risk analytics feedback loop by combining existing literature to generate shared core terminology and techniques. Second, by providing a novel understanding of cyber network risk and the role of AI in future CPS, we capture best practices and spark debate among practitioners and academia. Throughout the article, this architecture is explored and may be used as a best practice for designing and prototyping AI-enabled dynamic cyber risk analyses [3].2. Artificial intelligence, CPS, and predictive cyber risk analytics literature reviewThe IoT has been defined as a revolutionary technological augmentation that transforms the traditional living into a high-tech lifestyle in terms of data streams. CPSs and IoT generate massive amounts of data, necessitating powerful analytical tools for analysis. We almost likely need AI-assisted analytical tools to clean up the data’s noise and inconsistencies. On the other hand, CPS architectures cover a wide range of topics. These many notions must be integrated into a system [4].Furthermore, CPS mandates anti-counterfeiting and supply chain risk management to combat malicious supply chain components that have been altered from their original design to create disruption or perform illegal functions. Hyper-connectivity in the digital supply chain must be promoted in addition to design and process standardization. It is proposed that restricting source code access to critical and experienced employees can offer software assurance and application security and may be required to prevent the introduction of purposeful faults and vulnerabilities in CPSs. Forensics, prognostics, and recovery plans should be included in security measures for cyber-attack analysis and coordination with other CPSs and entities that detect external cyber-attack vectors [5]. An internal track and trace network procedure can help by recognizing or avoiding gaps in logistical security measures. To prevent the exploitation of CPS vulnerabilities discovered by reverse engineering assaults, a method for anti-malicious and anti-tamper system engineering is required. Taxonomic analysis was performed using the Smart literature review approach based on latent Dirichlet allocation. The resulting areas of concentration are organized into a taxonomy with acronyms to aid in the integration of artificial intelligence with the current CPS. Deep learning (DL) is a subset of machine learning (ML) and artificial intelligence (AI), and it is one of the primary technologies of the Fourth Industrial Revolution. It is derived from an artificial neural network (ANN) (Industry 4.0) [6]. ”Cyber security” and ”Deep learning” are becoming increasingly popular worldwide, as demonstrated in Figure 1.