The recent White House report on artificial intelligence (AI) highlights the importance of AI and the need for a clear roadmap and strategic investment in this area. As AI emerges from science fiction to become the frontier of world-changing technologies, there is an urgent need to systematically develop and implement AI to see its real impact in the next generation of industrial systems, known as Industry 4.0. This article provides an overview of the current state of AI in industrial applications and offers our contribution to the deployment of AI in cybersecurity for Industry 4.0.

Climate change and industrial development have brought greater uncertainty to water resources, and the quality of water has a very significant impact on humans and the entire ecosystem. The current water quality testing relies on the data collected by various monitoring systems, some of which are not immediately available or require more expensive equipment to analyze. Most experts agree that the amount of dissolved oxygen (DO) in the water is the main indicator for judging the quality of water. However, the process of obtaining information is more complicated and cumbersome. If the difficulty of obtaining the information can be simplified, it will make water resources better. Management is more efficient. In recent years, artificial intelligence is often developed to assist in many complex decision-making tasks. We develop a prediction model based on LSTM. We design a machine learning model and provide a large amount of data to make it find the rules and learn from it. Improve the predictive ability of the model. Through the model, the water quality can be monitored and analyzed, and the data obtained can be used to judge and predict the water quality state and deal with water pollution problems in time.

The aim of this work was to determine best linear model Adaptive Neuro-Fuzzy Inference System (ANFIS) and Sensitivity Analysis in order to predict the energy consumption for land leveling. In this research effects of various soil properties such as Embankment Volume, Soil Compressibility Factor, Specific Gravity, Moisture Content, Slope, Sand Percent, and Soil Swelling Index in energy consumption were investigated. The study was consisted of 90 samples were collected from 3 different regions. The grid size was set 20 m in 20 m (20*20) from a farmland in Karaj province of Iran. The values of RMSE and R2 derived by ICA-ANN model were, to Labor Energy (0.0146 and 0.9987), Fuel energy (0.0322 and 0.9975), Total Machinery Cost (0.0248 and 0.9963), Total Machinery Energy (0.0161 and 0.9987) respectively, while these parameters for multivariate regression model were, to Labor Energy (0.1394 and 0.9008), Fuel energy (0.1514 and 0.8913), Total Machinery Cost (TMC) (0.1492 and 0.9128), Total Machinery Energy (0.1378 and 0.9103).Respectively, while these parameters for ANN model were, to Labor Energy (0.0159 and 0.9990), Fuel energy (0.0206 and 0.9983), Total Machinery Cost (0.0287 and 0.9966), Total Machinery Energy (0.0157 and 0.9990) respectively, while these parameters for Sensitivity analysis model were, to Labor Energy (0.1899 and 0.8631), Fuel energy (0.8562 and 0.0206), Total Machinery Cost (0.1946 and 0.8581), Total Machinery Energy (0.1892 and 0.8437) respectively, respectively, while these parameters for ANFIS model were, to Labor Energy (0.0159 and 0.9990), Fuel energy (0.0206 and 0.9983), Total Machinery Cost (0.0287 and 0.9966), Total Machinery Energy (0.0157 and 0.9990) respectively, Results showed that ICA_ANN with seven neurons in hidden layer had better. According to the results of Sensitivity Analysis, only three parameters; Density, Soil Compressibility Factor and, Embankment Volume Index had significant effect on fuel consumption. According to the results of regression, only three parameters; Slope, Cut-Fill Volume

(V) and, Soil Swelling Index (SSI) had significant effect on energy consumption. Using adaptive neuro-fuzzy inference system for prediction of labor energy, fuel energy, total machinery cost, and total machinery energy can be successfully demonstrated.

Objective: By the lack of initiative by force (Faith) Iron man lives. Iron deficiency causes anemia, anemia causes dementia, Alzheimer dementia and Alzheimer’s produces cognitive impairment in memory produces bases. Well hear him. The Iron Will Alkaline, the answer is yes.

Methodology: On the basis of Love and the use of Iron and its allies, which are the B vitamins, Vitamin C, E and vitamin A. It is necessary to remember that there is to try to fight the greatest sustenance Anemia in all its contrarestantes.

Conclusion: The theory focuses on the oxygenation of the blood, which must be done, where the Warburg Alkaline Diet is demonstrated, among other factors it is necessary to emphasize the oxygenation that consists of the mental and physical, which is reduced in Sleeping correctly, Warburg Alkaline Diet, Drink Enough Water, Make Walks or Moderate Exercises, Comfort and Drink Iron, Vitamin C, Vitamin E, Complex B and Vitamin A. All this consists in Producing New Oxygen.

Although numerous profound learning models had been proposed, this research article added to symbolize the investigation of significant deep learning models on the sensible IoT gadgets to perform online protection in IoT by using the realistic Iot-23 dataset. It is a recent network traffic dataset from IoT appliances. IoT gadgets are utilized in various program applications such as domestic, commercial mechanization, and various forms of wearable technologies. IoT security is more critical than network security because of its massive attack surface and multiplied weak spot of IoT gadgets. Universally, the general amount of IoT gadgets conveyed by 2025 is foreseen to achieve 41600 million. So we would like to conduct IoT intrusion and anomaly detection systems of detecting IoT-based attacks by introducing various deep learning models on artificial neural networks such as Recurrent Neural Networks, Convolutional  Neural Networks, Multilayer Perceptron, Supervised GAN Adversarial Network, etc in both binary and multiclass classification modes in IoT- cybersecurity. We generate wide performance metric scores such as Accuracy, false alarm rate, detection rate, loss function, and Mean Absolute error.

The main focus of this talk is "human-AI teaming", specifically the mode of "human-AI collaboration" where humans and AIRL-based agents accomplish tasks together in a multi-agent system. Therefore, the objective cannot be achieved by just a lone human or agent, and the responsibilities in the environment are partitioned and/or shared between humans and agents. Collaborative multi-agent reinforcement learning (MARL) as a specific category of reinforcement learning (RL) provides effective results with agents learning from their observations, received rewards, and internal interactions between agents. However, centralized learning methods with a joint global policy in a highly dynamic environment present unique challenges in dealing with large amounts of information. This study proposes two innovative solutions to address the complexities of a collaboration between human and multiple RL-based agents (referred to hereafter as “Human-MARL teaming”) where the goals pursued cannot be achieved by a human alone or agents alone. The first innovation is the introduction of a new open-source MARL framework, called COGMENT, to unite humans and agents in real-time complex dynamic systems and efficiently leverage their interactions as a source of learning. The second innovation is our proposal of a new hybrid MARL method, named Dueling Double Deep Q learning MADDPG (D3-MADDPG) to allow agents to train decentralized policies parallelly in a joint centralized policy. This method can solve the overestimation problem in Q-learning methods of value-based MARL. We demonstrate these innovations by using a designed real-time environment with unmanned aerial vehicles driven by RL agents, collaborating with a human to fight fires. The team of RL agent drones autonomously looks for fire seats and the human pilot douses the fires. The results of this study show that the proposed collaborative paradigm and the open-source framework leads to significant reductions in both human effort and exploration costs. Also, the results of the proposed hybrid MARL method shows that it effectively improves the learning process to achieve more reliable Q-values for each action, by decoupling the estimation between state value and advantage value.

With the exponential rise of AI usage in Banks, many financial organizations are still struggling with building efficient Model Development Life Cycles (MDLC) and the means to expedite business value realization and return of investment (ROI). There are several contributing factors which can give rise in less than optimal MDLC, such as, lack of proper data governance and processes around it as well as lack of performant AI solutions and platforms. In this session, you will learn how to use most essential business value accelerators (BVA) to expedite Data Science Discovery, Data Ingestion, and Model Development leading to most optimal Model Business integration. This session will provide lots of valuable and real to life strategies and executable plans to help reduce your MDLC and time to market by at least 50%

The advancements of computer science and its related fields are making our tasks easier in almost every scientific and non-scientific field. The use of machine learning in the field of drug discovery and development is accelerating so fast and helping us to discover anti-viral drugs for devastating viruses like coronavirus. The author will discuss using a deep reinforcement learning model 'ORGAN' which is a modified version of Generative Adversarial Network for predicting the potential anti-viral of coronavirus. The author used the deep reinforcement learning model (ORGAN) to generate potential candidates’ drugs, with a λ of 0.2 and epochs of 240 and a sample set of 6400, 10 good sample SMILES were generated and the Solubility or LogP of these samples is 0.7098. Then using the coronavirus as a target, all the good samples of SMILES were bounded and the drug with the highest binding affinity (Most negative value) is C18H15ClN4O2 also known as Olutasidenib which can be the potential anti-viral drug of coronavirus.

Technology has become an essential aspect of law enforcement routine, helping police officers on solving, preventing and even predicting criminal activity globally. Artificial Intelligence is one of many important tools police can rely on. The harmonic integration between men and machine is now an essential part for operations success on security enforcement. How artificial intelligence can address criminal justice needs? Which innovations we have available to improve public safety? This article will demonstrate how artificial intelligence (AI) has became a major resource in numerous ways. It is now the ultimate solution for criminal justice, based on big data, algorithmics and machine learning to detect different patterns on human behavior. Those solutions are mainly based on pattern identification, image scanning, face recognition, sociodemographic analysis, voice parameters, actions, conducts, movements, biometrics and even emotions acknowledgement, which are now being considered an excellent evidence for deception detection, fraud, violence and terrorists acts. It is also used on DNA documentation, ballistics and profiling. Unlike humans, machines do not tire. On the opposite, it is proven on several ways, to be better than humans. It is confirmed machines are very good on identifying anomalous patterns and learning new patterns faster than humans. AI technologies provide the capacity to detect, predict and evaluate, overcoming errors and present virtuous results. The more amount of data, more precise will be the outcome. AI algorithms can potentially be used as a very efficient observer, increasing the accuracy of police officers on their complex daily routine. Predictive analysis (ex. PREDPOL) is one of many examples we will show to demonstrate how important those solutions subsist and innovate the security context. Those systems process large volumes of information simultaneously, providing precise outcomes. This article will deeply investigate and compare several platforms used by different law enforcement units around the globe, pointing new solutions, challenges and potential developments needed. As a conclusion, we have noticed how important was the introduction of AI on law enforcement routine, performing risk evaluations, crime solutions and delinquency prevention.

Improving cities is a pressing global need as the world’s population grows and our species becomes rapidly more urbanized. In 1900 just 14 percent of people on earth lived in cities but by 2008 half the world’s population lived in urban areas. Today, 55% of the world’s population lives in urban areas and this percentage is expected to rise to 68% by 2050. The use of artificial intelligence in smart cities can be life-changing if implemented in the right spaces. There are multiple zones in cities or in urban development where AI can be used to improve the performance and efficiency of the system. AI has the ability to understand how cities are being used and how they are functioning. It assists city planners in comprehending how the city is responding to various changes and initiatives. AI with the help of Deep Learning and Computer Vision has changed the way vehicle analytics is done. With these advancements, vehicle analytics is helping in implementing intriguing solutions like Toll booth automation, Smart parking, Gate security, ATCS (Adaptive Traffic Control System), RLVD (Red Light Violation Detection) etc. This talk starts by briefing about what's AI based vehicle analytics and what all it includes, and goes on to talk about varieties of applications of vehicle analytics including implementation and deployment challenges. Towards the end talk focuses on why it's need of the hour for this populated, industrialised and tech-driven era.